In-depth accident causation database and analysis report - ERSO

Deliverable 5.8: In-depth accident 

causation database and analysis report 

Contract No: TREN-04-FP6TR-SI2.395465/506723 

Acronym: SafetyNet 

Title: Building the European Road Safety Observatory 

Integrated Project, Thematic Priority 6.2 “Sustainable Surface 

Transport” 

Project Co-ordinator: 

Professor Pete Thomas 

Vehicle Safety Research Centre 

Ergonomics and Safety Research Institute 

Loughborough University 

Holywell Building 

Holywell Way 

Loughborough 

LE11 3UZ 

Organisation name of lead contractor for this deliverable: 

Chalmers University of Technology 

Due Date of Deliverable: 30 October 2008 

Submission Date: 04 December 2008 

Report Author(s): K. Björkman, H. Fagerlind, M. Ljung Aust, E. Liljegren 

(Chalmers); A. Morris, R. Talbot, R. Danton (VSRC); G. Giustiniani, 

D. Shingo Usami (DITS); K. Parkkari (VALT); M. Jaensch (MUH); 

E. Verschragen (TNO) 

Project Start Date: 1st May 2004 

Duration: 4, 5 years 

Project co-funded by the European Commission within the Sixth Framework Programme (2002 -2006) 

Dissemination Level 

PU Public 

PP 

RE 

CO 

Restricted to other programme participants (inc. Commission Services) 

Restricted to group specified by consortium (inc. Commission Services) 

Confidential only for members of the consortium (inc. Commission Services) 

Project co-financed by the European Commission, Directorate-General Transport and Energy

D 5.8: In-depth accident causation database and analysis report 

Executive Summary 

The SafetyNet project is an Integrated Project (IP) which was developed as part of 

the European Commission’s 6th Framework programme. SafetyNet has built the 

foundations of a European Road Safety Observatory (ERSO) which can be used by 

the European Commission for the purposes of policy review and development. The 

SafetyNet project is divided into seven main Work Packages each of which deal with 

specific aspects of road safety research (see www.erso.eu). 

The objective of the SafetyNet Work Package 5, Task 2 was to develop an in-depth 

European accident causation database to identify risk factors that contribute to road 

accidents. To assist in the analysis of the accident causation a method, known as 

SNACS, was further developed, tested and revised throughout the project. The 

accident investigations were performed by existing multidisciplinary teams within the 

partnership which have many years of experience. 

The accident causation database was developed in two parts; a set of general 

variables about the accident, vehicle, road environment and road users and a part 

which was dedicated to the accident causation analysis performed with the SafetyNet 

Accident Causation System (SNACS). The definitions for the general variables and 

values as well as the SNACS method were piloted and revised several times before 

data collection commenced to ensure high quality in the gathered data. 

In total, 1006 accident cases were investigated which include 1833 vehicles and 

pedestrians. In the aggregated analysis these vehicles were grouped according to 

their trajectory prior to the accident and the groups were; Vehicles leaving their lane 

(n = 354), Vehicles encountering something in their lane (n = 537), Vehicles 

encountering another vehicle on crossing paths (n = 528) and Accidents involving 

slower moving vulnerable road users (n = 92 pedestrians; 95 Pedal Cyclists, 177 

opponents) 

The aim of the analyses conducted was not to explore and evaluate the effectiveness 

of new technologies, but rather to demonstrate the potential uses for the accident 

causation database and identify common accident scenarios. The SNACS charts in 

the groups were aggregated to allow the most commonly occurring accident 

contributing factors to be identified. In the SNACS charts the information is rich and 

detailed and it is by nature complex as it reflects the complex interactions between 

the road users, vehicles and environment that occur in an accident. The SNACS 

method assists in the process of identifying patterns that will allow the most common 

accident contributing factors to be focused on when designing countermeasures. 

Project co-financed by the European Commission, Directorate-General Transport and Energy 

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Abbreviations and definitions 

Abbreviations 

ACASS 

ERSO 

CARE 

Chalmers 

DITS 

DREAM 

FR 

GDV 

GIDAS 

LTAP-LD 

LTAP-OD 

LTIP 

MUH 

O 

OTS 

RF 

RTIP 

S 

SCP 

SNACS 

SVRU 

TNO 

VALT 

VSRC 

Accident Causation Analysis with Seven Steps 

European Road Safety Observatory 

Community database on Accidents on the Roads in Europe 

Chalmers University of Technology, Sweden 

Department ‘Idraulica Transporti Strade’ at University of Rome “La 

Sapienza”, Italy 

Driving Reliability and Error Analysis Method 

In section 3.2 Striking vehicle in front 

German Insurance Industry 

German in-Depth-Accident Study 

Left Turn Across Path-Lateral Direction 

Left Turn Across Path-Opposite Direction 

Left Turn Into Path 

Medical University of Hannover, Germany 

In section 3.2 Striking object other than vehicle in front 

On-The-Spot accident research 

In section 3.2 Being struck from behind 

Right Turn Into Path 

In section 3.2, Being struck by a vehicle which has left its lane 

Straight Crossing Paths 

SafetyNet Accident Causation System 

Slower Moving Vulnerable Road Users 

Netherlands Organisation for Applied Scientific Research, The 

Netherlands 

Finnish Motor Insurers' Centre, Finland 

Vehicle Safety Research Centre, United Kingdom 


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Definitions 

Accident investigation Acquisition of factual information regarding an accident (note: can 

include on-scene elements, elements recorded retrospectively, or 

both of these) [ISO 12353-1:2002]. 

SafetyNet comment: It includes both “data collection” and “case 

analysis”. 

Accident notification A message from the emergency service is sent to the on-scene 

team when an accident occurred. 

Accident scene The area of a traffic accident before the vehicles and people 

involved have left [ISO 12353-1:2002]. 

Accident site The geographic location of the accident scene (note: the accident 

site may be given as exact coordinates or in a less detailed way) 

[ISO 12353-1:2002]. 

Case 

A case is a separate accident that has been chosen for accident 

investigation. Each accident investigation is treated as a case. 

Case analysis The analysis of one specific accident using the data collected, 

performed by investigators. 

Data analysis 

Data collection 

Investigation team 

Investigator 

On-scene (accident) 

investigation 

On-scene team 

Retrospective 

(accident) 

investigation 

Retrospective 

inspection 

Retrospective team 

Sampling criteria 

Aggregate analysis of all or selected cases in the database. 

Objective data collected on-scene, retrospectively or data retrieved 

from other sources. Data collection also includes subjective 

information, such as interviews. 

A multidisciplinary group of investigators investigating a specific 

accident. 

A person with expert knowledge in one or more areas of accident 

investigation 

Accident investigation conducted at the accident scene with the 

purpose of collecting on-scene information before physical evidence 

(e.g. the vehicles involved) has been removed [ISO 12353-1:2002]. 

A team of investigators who are ready to respond to an accident 

notification and perform on-scene investigations. 

A complete accident investigation conducted retrospectively, i.e. no 

on-scene investigation is conducted. 

When an on-scene accident investigation has been conducted, 

retrospective inspections of vehicles is conducted. 

A team of investigators who are performing retrospective 

investigations or retrospective inspections. 

Principals of evaluation of scope and coverage of an accident 

investigation referring to different aspects [ISO 12353-1:2002]. 

The terms and definitions taken from ISO 12353-1:2002 Road Vehicles - Traffic accident 

analyses, Part 1: Vocabulary, are reproduced with permission of the International 

Organization for Standardization, ISO. This standard can be obtained from any ISO member 

and from the Web site of ISO Central Secretariat at the following address: www.iso.org. 

Copyright remains with ISO." 


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Table of Content 

Executive Summary ..................................................................................................... I 

Abbreviations and definitions ...................................................................................... II 

Abbreviations .......................................................................................................... II 

Definitions .............................................................................................................. III 

Table of Content ........................................................................................................ IV 

1 Introduction .......................................................................................................... 1 

1.1 Background ................................................................................................... 1 

1.2 Objectives of SafetyNet Accident Causation Database ................................. 2 

1.3 Partners involved ........................................................................................... 2 

2 Accident investigation methods ........................................................................... 3 

2.1 Overview of the database .............................................................................. 3 

2.2 Data collection ............................................................................................... 5 

2.2.1 Germany (MUH)...................................................................................... 6 

2.2.2 Italy (DITS).............................................................................................. 8 

2.2.3 The Netherlands (TNO) .......................................................................... 9 

2.2.4 Finland (VALT)...................................................................................... 11 

2.2.5 Sweden (Chalmers) .............................................................................. 12 

2.2.6 UK (VSRC)............................................................................................ 13 

2.2.7 Summary .............................................................................................. 15 

2.3 Introduction to SNACS - SafetyNet Accident Causation System ................. 16 

3 Aggregated SNACS-data analysis .................................................................... 19 

3.1 Vehicle leaving its lane ................................................................................ 22 

3.1.1 Sorting .................................................................................................. 24 

3.1.2 Analysis and results .............................................................................. 24 

3.1.3 Discussion and conclusions .................................................................. 40 

3.2 Vehicle encountering something in its lane, either in front or from the rear . 41 

3.2.1 Sorting .................................................................................................. 42 

3.2.2 Analysis ................................................................................................ 43 

3.2.3 Results .................................................................................................. 44 

3.2.4 Discussion and conclusions .................................................................. 60 

3.3 Vehicle encountering another vehicle on crossing paths ............................. 63 

3.3.1 Sorting .................................................................................................. 65 

3.3.2 Analysis ................................................................................................ 66 

3.3.3 Results .................................................................................................. 67 

3.3.4 Discussion............................................................................................. 83 

3.3.5 Conclusions .......................................................................................... 86 

3.4 Accidents involving vulnerable road users ................................................... 86 

3.4.1 Sorting .................................................................................................. 87 

3.4.2 Analysis ................................................................................................ 87 

3.4.3 Results .................................................................................................. 91 

3.4.4 Discussion and conclusions ................................................................ 116 


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3.5 Aggregated analysis summary .................................................................. 117 

3.5.1 Vehicle leaving its lane ....................................................................... 117 

3.5.2 Vehicle encountering something in its lane, either in front or from the rear 

118 

3.5.3 Vehicle encountering another vehicle on crossing paths .................... 119 

3.5.4 Accidents involving Slower moving Vulnerable Road Users ............... 119 

4 Small scale study comparing cases analysed with SNACS and ACASS 

respectively ............................................................................................................. 121 

4.1 Introduction to Accident Causation Analysis with Seven Steps – ACASS . 121 

4.2 Comparing case analysed with SNACS and ACASS respectively............. 122 

4.2.1 Data analysis of cases analysed with the ACASS method ................. 123 

4.2.2 Data analysis of cases analysed with the SNACS method ................. 125 

4.2.3 Discussion........................................................................................... 126 

5 General discussion .......................................................................................... 127 

6 Conclusions ..................................................................................................... 129 

7 References ...................................................................................................... 130 

Appendices 

APPENDIX A: SNACS linking table with glossary for Phenotypes and Genotypes 

APPENDIX B: How to sort the accidents 

APPENDIX C: List of ACASS codes 


Page V


1 Introduction 

The SafetyNet project is an Integrated Project (IP) which was developed as part of 

the European Commission’s 6th Framework programme. SafetyNet has built the 

foundations of a European Road Safety Observatory (ERSO) which can be used by 

the European Commission for the purposes of policy review and development. The 

SafetyNet project is divided into seven main Work Packages each of which deal with 

specific aspects of road safety research (ERSO, 2008). 

This deliverable describes the second task of Work Package 5 of SafetyNet which 

involves the development of a method for assessment of accident contributing factors 

and an accident causation database including 1006 individual cases. The accidents 

were investigated using an analysis approach known as the SafetyNet Accident 

Causation System (SNACS) (Reed and Morris, 2008) to classify the contributing 

factors that lead to the crash. The report briefly describes the methods used to collect 

the data and the case analysis procedures. However, the emphasis of this report is 

dedicated to the aggregation of cases and the outcome from the data analysis. 

In addition to the accident investigation activities, a small scale comparative study 

was performed between cases analysed with the developed method within SafetyNet 

(SNACS) and the Accident Causation Analysis with Seven Steps (ACASS) method 

used in investigations at the Medical University of Hanover (MUH). 

1.1 Background 

Fatalities and injuries due to traffic accidents are one of the major health problems in 

the world today. About 10 million people get injured in traffic accidents every year 

and the number will probably rise due to population growth and increase of mobility 

(Peden et al., 2001) 

The European Commission stated in the Road Safety Strategy that the number of 

fatalities in the EU-25 member states should decrease by 50 percent by the year 

2010 (European Communities, 2001). To meet this target there is a need for better 

understanding of why accidents happen. One tool to enhance this knowledge is to 

perform accident investigations by data collection from the accident scene and 

interview involved road users. The data is then put together in a case analysis to find 

contributing factors leading to the accident. It is interesting both to gain knowledge 

about the performance of the in-vehicle technological systems aimed at accident 

mitigation as well as the human behaviour in different road environments. Accident 

data is needed both to assess the performance of existing systems but also as a 

support in the development of future systems. 


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1.2 Objectives of SafetyNet Accident Causation Database 

There were two main objectives for the SafetyNet Work Package 5, Task 2; 

- to develop an in-depth European accident causation database to identify risk 

factors that contribute to road accidents. 

- to develop a method to assist the investigators in the analysis of the accident 

for better understanding and categorising of accident contributing factors 

The objectives of this report is to give an short overview of the methodology used for 

accident investigation and to present selected results of the aggregation of data from 

SafetyNet Accident causation Database. 

1.3 Partners involved 

Six partners were involved in the development of the accident causation database. 

The partners represented six countries within the European Union and are 

independent groups with no interest in commercial aspects of the study outcomes. 

The partners were (Figure 1): 

• Germany: Medical University of Hannover (MUH) 

• Italy: Department ‘Idraulica Transporti Strade’ at University of Rome “La 

Sapienza” (DITS) 

• The Netherlands: Netherlands Organisation for Applied Scientific Research 

(TNO) 

• Finland: Finnish Motor Insurers' Centre (VALT) 

• Sweden: Chalmers University of Technology (Chalmers) 

• United Kingdom: Vehicle Safety Research Centre (VSRC) 

Figure 1, Project teams 


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2 Accident investigation methods 

The accident data collected within the project was collected by six separate teams 

between 2005 and 2008. A combination of “on-scene” and “nearly on scene” 

methods were used and multidisciplinary teams investigated the accidents following 

accident notification by going on site and conducting vehicle- and road inspections as 

well as interviews with the crash participants. The data collection method is described 

in section 2.2. 

The accident causation database was developed in two parts. A set of general 

variables about the accident, vehicle, road environment and road users was 

developed in conjunction with the Work Package 5.1 Fatal accident database and 

these variables are common for both databases. The second part which is specific to 

the accident causation database was the development of a European method for 

recording accident causation information. The method developed was SNACS 

(SafetyNet Accident Causation System), the development of which is documented in 

section 2.3. 

The definitions for the general variables and values as well as SNACS were piloted 

and revised several times before data collection commenced to ensure high quality in 

the gathered data. More detailed information about the pilot study can be found in 

Paulsson and Fagerlind (2006). 

2.1 Overview of the database 

In total, the database contains 1006 cases, 1833 vehicles and 2428 road users. The 

number of cases investigated by each team is presented in Table 1. Most of the 

vehicles involved in the accidents are passenger cars (64%); the second largest 

category is motorized two-wheelers (10%). The remaining vehicles are scattered over 

the other categories and no category is larger than 7%. (Table 2) 

Table 1, Contribution to the accident causation database by partner 

SafetyNet Accident Causation Database 

Participant ID Germany Italy The Finland Sweden UK Total 

Netherlands 

Cases 100 260 126 200 70 250 1006 


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Table 2, Vehicles in the accident causation database 

Vehicle Type Vehicles % 

Agricultural vehicle 5 0% 

Bicycle 96 5% 

Bus / Minibus 36 2% 

Car / MPV 1169 64% 

Motorcycle / Moped 179 10% 

Other 18 1% 

Shoe vehicle (pedestrian) 91 5% 

Train / Tram 10 1% 

Truck 137 7% 

Van 91 5% 

Unknown 1 0% 

Total 1833 

Most of the accidents in the database occur in daytime between 06.00 and 18.00 and 

therefore most of the vehicles had an accident time between these hours (Table 3). 

Table 3, Time of the day when the accident occurred, presented per vehicle involved 

Time Vehicles % 

00:00-05:59 71 4% 

06:00-11:59 706 39% 

12:00-17:59 775 42% 

18:00-23:59 281 15% 

Total 1833 

Data connected to the road environment has been summarized in Table 4 - Table 6. 

Most involved vehicles drove on a road which has a speed limit of between 50 and 

90 kph and is located in an urban area. Accidents also occurred on straight roads 

(68%) more often than the other categories. 

Table 4, Speed limit on the road of the involved vehicle 

Speed limit Vehicles % 

90 kph 259 14% 

Unknown 25 1% 

Total 1833 


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Table 5, Local area of the accident 

Local Area Vehicles % 

Mixed 165 9% 

Rural 573 31% 

Urban 1094 60% 

Unknown 1 0% 

Total 1833 

Table 6, Horizontal alignment on the road of the involved vehicles 

Horizontal Alignment Vehicles % 

Straight road 1239 68% 

Bend to left 230 13% 

Bend to right 226 12% 

Junction 71 4% 

Other 67 4% 

Total 1833 

If the age of the drivers is divided into four groups, the largest category is the 25-44 

age range. Older drivers are the smallest group represented in the database (see 

Table 7). 

Table 7, Age of the driver of the involved vehicles 

Age Vehicles % 


Three tools were developed to guide the teams: 

• Data collection protocol (general variables), includes variables handling the 

accident site, the road environment, the vehicle(s) and the road user(s) 

involved. 

• SNACS 1.2, an analysis method to categorise contributing factors to the 

accident occurrence (see Section 2.3) 

• SafetyNet Accident Causation Database, for input and storage of the 

collected and analysed data 

A brief description of the investigation methods employed by each investigation team 

and a summary of the differences between them can be found in the in the following 

sections (2.1.1-2.1.5) 

2.2.1 Germany (MUH) 

Sampling area: 

The region of the data acquisition is the region of Hannover which is located in the 

federal state of Lower Saxony (see Figure 2). With 2290 square km the region of 

Hannover is about 5% the size of Lower Saxony (47618 square km) and with 1.13 

million inhabitants the region of Hannover has about 14% of the population of Lower 

Saxony (8 million inhabitants). 

Figure 2, Germany and the Region of Hannover in the state of Lower Saxony 


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Data was collected on accidents, which were documented by the German in-Depth- 

Accident Study (GIDAS) accident investigation team in Hannover. Thus all types of 

accidents on public roads with at least one injured accident participant were 

collected. For the SafetyNet Accident Causation Database the data was collected 

mainly on accidents that happened during daytime hours between 2006 and 2008 

Accident notification 

The GIDAS accident investigation team is automatically notified by the computer of 

the rescue services or by the police. 

Type of investigation 

The standard GIDAS accident investigation team consists of two technicians and one 

physician and collects in depth data on various fields from vehicle damages, 

environmental conditions, traces, personal information, injuries to accident causation 

information 

For SafetyNet an additional specially trained member was added to the team to 

conduct the accident causation interviews for SafetyNet on scene or in hospital. 


After the notification the accident investigation team went to the accident site with two 

special response vehicles with flashing blue lights to arrive at the scene as soon as 

possible. While the technical part of the team collected all the relevant data about the 

vehicles involved and information on the accident site, the physician together with the 

specially trained member for SafetyNet collected personal data and injury information 

from the involved persons as well as information on the causes of the accident. 

If accident participants were not available for interview at the scene of the accident, 

they were interviewed in hospital directly after the occurrence of the accident or in 

some cases they were interviewed retrospectively on the phone 

SNACS analysis 

The SNACS case analysis was in most cases completed by the investigator who 

conducted the interview with the road user. 


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2.2.2 Italy (DITS) 

Sampling area 

The cases were collected in the Marche Region that is located in the central-east 

area of Italy, see Figure 3. 

Figure 3, Italy and the Marche Region in dark red 

Marche Region has a population of approximately 1,5 million people and an 

extension of about 9,600 km 2 compared to a total national population of 60 million 

and a national extension of about 301,300 km 2 . 


All different accident types as well as all different vehicles were part of the sample but 

an ambulance had to be called to the accident scene for the investigation team to get 

notified. The alarms were received round the clock and the accident was investigated 

on scene within 30 minutes from the accident notification. 

Accident notification and data collection methodology 

Once an accident occurred, the Rescue Service was informed by the involved road 

users and an ambulance was sent on-site. The Rescue Service informed, by phone, 

the road safety technicians in service and they drove to the accident site to collect all 

the accident data. A scheme of the notification process can be seen in Figure 4. 


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Figure 4, Notification process 

There were 13 teams covering all the Marche Region and normally a team was on 

site 30 minutes or less after being informed by the rescue service. The road safety 

technicians are employed in the Regional Health Service. 

On-site the road safety technicians collected all the data available about the road, the 

involved vehicles and the weather conditions. The interviews were most often 

conducted on-site or at the hospital, almost never retrospectively. 

SNACS case analysis 

Once all the data about the accident were collected it was sent to DITS. The DITS 

personnel analysed the data, inserted it into the Database and conducted the 

SNACS analysis. When needed the technicians responsible for the accident data 

collection was contacted for more information or to clarify some points. 

2.2.3 The Netherlands (TNO) 

Sampling area 

The cases were collected within the region of Rotterdam Rijnmond in the county of 

South Holland (except for one case that was collected in The Hague, also in South 

Holland). The county has a population of approximately 3,5 million and is located in 

the south-west of the country; see Figure 5. 

Figure 5, The Netherlands, South Holland in orange 


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All different accident types as well as all different vehicles were part of the sample 

and the severity ranged from serious damage to the vehicles to accidents with a fatal 

outcome. The sample time for the retrospective cases was all hours of the day, all 

days of the week. The on-scene cases were collected on weekdays in normal 

working hours. 


The team visited the police office on a weekly basis and collected data from randomly 

picked out accidents for the retrospective cases. In the summer of 2007 the team 

was present at the police station all day and joined the police when they got noticed 

of a traffic accident, this resulted in approximately 20 on-scene accident 

investigations. 


Approximately 20 of the accidents were investigated with an on-scene methodology 

and 106 of the accidents were investigated with a retrospective methodology. 


In the case of an on-scene accident investigation, a team of two investigators would 

go out to the accident location and first establish contact with the police. They would 

chart the accident surroundings and take photos of them and the involved vehicles. 

Whenever possible, victims and witnesses would be interviewed for 'their story' of the 

accident. In the case of a retrospective accident investigation, personal data, photos 

of the accident scene and involved vehicles, and a full accident report of the special 

police unit on traffic accidents in the Rotterdam Rijnmond region were obtained. In 

both types of cases, a discussion between the investigators and the special trained 

police officers would often lead to a greater understanding of the accident causes. 

Furthermore, retrospective interviews with the people involved in the accident would 

be done by questionnaires. 


The SNACS analysis for each accident was done by one of the investigators who 

collected the accident data (either on-scene or retrospectively). In most cases the 

SNACS analysis was checked by one of the other investigation team members and 

periodically a case was picked out and then discussed with the whole team of 

investigators (4 members in total). 


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2.2.4 Finland (VALT) 

Sampling area 

The accidents were investigated in the area of continental Finland. There were 20 

investigation teams operating (see Figure 6) 

Figure 6, Areas of operation of the Finnish investigation teams. 


The investigated accidents were sampled from ongoing projects defined according to 

the annual action plan confirmed by the Ministry of Traffic and Communications. 

These included all fatal road and cross-country traffic accidents. In addition injury 

only accident programs running during the sampling time included: motorcycle 

accidents, single vehicle car accidents and accidents involving heavy goods vehicles. 

The alarms were received and investigated round the clock on all days of the week. 


The information about the accident was reported by the emergency centre or the 

local senior police officer to the investigation team. 


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Most accidents were investigated with an on-scene methodology. The accidents not 

investigated with an on-scene methodology have been investigated retrospectively. 

The reason for this was that the notification was delayed because of for example a 

later fatality due to serious injuries. 


The data was collected according to the published method on accident investigations 

made in Finland, the “VALT-method.” 


The SNACS analysis was made by two trained researchers at the VALT office on the 

basis of the VALT-method risk analyses made by the accident investigation team. In 

addition all data collected during the investigation was available for the researchers. 

2.2.5 Sweden (Chalmers) 

Sampling area 

Chalmers investigated accidents within the county of Västra Götaland, which is an 

area with a population of 1.5 million people (17% of the total population of Sweden) 

and 6% of the total area of Sweden. The sampling area was limited to approximately 

a 30 minutes drive from Gothenburg City Centre and included the city of Gothenburg 

as well as Mölndal, Härryda, Mölnlycke, Partille, Lerum, Ale and Kungälv 

municipalities, see Figure 7. The area includes urban as well as rural road networks 

and several roads with heavy traffic. 

Figure 7, County of Västra Götaland, Gothenburg with surroundings in the enlarged circle 


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The sample included all types of road accidents during 2006-2007 where an 

ambulance had been called to the scene. The accidents were investigated during 

weekdays during normal working hours 


The investigation team were notified by the emergency services as they occurred, 

within minutes of the accident taking place and being called in. Notifications were 

received 24 hours a day but only those received during on-call hours were responded 

to. 


All accidents were investigated with an on-scene methodology. 


The investigation team arrived to the accident scene within 30 minutes of its 

occurrence. They team established contact with the rescue services and police onscene. 

Data were collected about the accident site, the vehicles and the road users 

in the accident. Drivers and witnesses remaining at the scene were interviewed about 

their experience of the accident. An in-depth follow-up interview with the driver was 

performed by the investigators as soon as possible after the accident. No 

retrospective inspection of vehicles was performed. 


The SNACS analysis for each driver in the accident was performed by a trained 

investigator, normally the interviewer. The analysis was later discussed at a meeting 

including all investigators. 

2.2.6 UK (VSRC) 

Sampling area 

The VSRC’s cases were collected by the UK On-The-Spot (OTS) project. Accident 

investigators operated within the administration regions of Gelding, Broxtowe, 

Rushcliffe and Nottingham City Centre, see Figure 8. This area is broadly 

representative of the UK in terms of injury severity and involved road users. This area 

covers a road network of both rural and urban carriageways and varying road 

classifications. 


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Figure 8, England, Nottinghamshire in the enlarged circle 


Accident investigators aim to investigate all road accidents that are reported to the 

police during the periods of operation. This includes injury and non-injury accidents 

involving all road users and vehicle types. The OTS teams operate in a rotating shift 

pattern of 8 hours to cover all days of the week and hours of the day. 


The police member of the OTS team was notified of accidents by the police control 

room as they occurred, within minutes of the accident occurring and being reported to 

the police. 


All accidents were investigated with an on-scene methodology. 


The investigation team arrived at the accident scene within 20 minutes of its 

occurrence, allowing accurate data about the accident site, vehicles, road 

environment and involved road users to be collected. All remaining road users or 

witnesses were interviewed at scene, with a postal questionnaire being sent out to all 

accident participants with follow up questions. 


The SNACS case analysis was completed by the investigator who had investigated 

the accident and interviewed the involved road users. 


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2.2.7 Summary 

Sampling area 

• Nationwide: Finland 

• Region: Sweden, UK, Italy, Germany, The Netherlands 


Time 

• Ambulance called to the scene: Sweden, Italy 

• Police called to the scene: UK, The Netherlands 

• Accidents with at least one injured participant: Germany 

• Varied: Finland 

• All hours: UK, Finland, Italy, The Netherlands (retrospective) 

• Monday-Friday, Normal working hours: Sweden, Germany, The Netherlands 

(on-scene) 


• Emergency call-centre: Sweden, Italy, 

• Emergency call-centre and Police radio: Finland, Germany 

• Police control room: UK, The Netherlands 


• On-scene: Sweden, UK, Finland (partly), Italy, Germany, The Netherlands 

(partly 20 cases) 

• Retrospective: Finland (partly), The Netherlands (partly, 106 cases) 


• The data collection was performed by multidisciplinary teams in all countries 

SNACS analysis completion 

• SNACS analysis was completed by the investigator performing the data 

collection: Sweden, Germany, The Netherlands, UK 

• SNACS analysis was completed by an external analyst not included in the 

investigation team: Finland, Italy 


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2.3 Introduction to SNACS - SafetyNet Accident Causation 

System 

One of the main tasks within SafetyNet Work Package 5 was the development of a 

method to better understand the accident contributing factors. The analysis method 

SNACS (SafetyNet Accident Causation System) was developed and tested in 

conjunction with the SafetyNet activities. 

SNACS is a tool to classify the contributing factors that lead to the crash. The basic 

philosophy is that accidents happen when the dynamic interactions between people, 

technologies and organisations fail to meet the demands of the current situation in 

one way or another and that such failures are due to a combination of contributing 

factors which together generate the accident. 

The data analysis of the SafetyNet Accident Causation Database can be divided into 

two parts; analysis of individual cases and analysis of aggregated cases. While the 

analysis of an individual case results in a chart of interlinked contributing factors, the 

analysis of aggregated cases is performed by superimposing individual charts in 

order to find common causation patterns for a selected group of cases. The SNACS 

method is developed for individual case analysis and do not include a description 

how to perform aggregation of cases. It is rather up to each analyst to decide how to 

aggregate the cases. The method and results of the aggregation analysis of the 

SafetyNet causation database, is presented in Section 3 

The analysis of an individual case is performed on vehicle level (including 

pedestrians) and is based on the information collected from the accident scene and 

interviews with involved drivers. The accidents stored in the database were 

investigated using SNACS version 1.2 (published in Reed and Morris, 2008). A short 

description of individual case analysis with SNACS is presented below, please see 

the SNACS 1.2 manual for detailed understanding of the method. 

The SNACS classification scheme distinguishes between observable effects in the 

form of human actions or system events (critical events/phenotypes) and the factors 

(causes/genotypes) that may cause them. The critical events are expressed in the 

general dimensions of time, space and energy, and are closely related to the 

transition phase between risk and emergency situations. In SNACS version 1.2, the 

causes are divided into 16 different main categories of factors, placed in four main 

classes; Road User, Vehicle, Infrastructure and Organisation (see Table 8) 

The classification scheme is illustrated in detail in Appendix A. 


Page 16


Table 8, The main categories of causes/genotypes and critical events/phenotypes (indicated by 

the letters A, B, C, etc) in the classification scheme of SNACS version 1.2 

Genotypes (Contributing factors) 

A: Phenotypes 

Organisation Infrastructure Vehicle Road User 

(Observable 

effects) 

M: Organisation J: Communication 

driver-environment 

K: Maintenance-road 

N: Road design 

G: Temporary HMI 

problems 

H: Permanent HMI 

problems 

I: Equipment 

K: Maintenancevehicle 

O: Vehicle design 

B: Observation 

C: Interpretation 

D: Planning 

E: Temporary 

Personal Factors 

F: Permanent 

Personal Factors 

J: Communication 

driver-driver 

L: 

Experience/training 

1: Timing 

2: Duration 

3: Force 

4: Distance 

5: Speed 

6: Direction 

7: Object 

8: Sequence 

In addition to listing critical events and causes, the classification scheme also 

describes possible links between them. The links represent the ways in which 

different factors can affect each other. The links render the classification scheme 

non-hierarchical, but provide depth to and guidance for the analysis. The links 

creates different causation chains which can be assigned a level of confidence. The 

analyst can chose between low, reasonable and high level of confidence depending 

on available data, the quality of the data, agreement of data from different sources 

etc. 

A SNACS chart is compiled by first assigning the critical event and then the factors 

deemed appropriate on the basis of the available accident-case information. 

However, the assignment of factors cannot be made arbitrarily but is restricted as 

well as guided by the links in the classification scheme. The result of a SNACS 

analysis is a chart illustrating multi-linear sequences of interlinked factors that 

account for the way in which the analysis was made and conclusions about how the 

factors contributed to the crash event. For more in-depth information about the 

method, see SNACS version 1.2 which published in D5.5 Glossary of Data Variables 

for Fatal and Accident causation databases (Reed and Morris, 2008). In Figure 9 

visual examples of SNACS charts are illustrated. 


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Example 1 Example 2 

Specific genotype 

General genotype General genotype Phenotype 

Specific Genotype 

General genotype 



Phenotype 








Example 3 Example 4 


General genotype General genotype Phenotype 

Specific Genotype General genotype General genotype General genotype Phenotype 






Figure 9, Visual examples of SNACS charts 

There was a need to define a standard SNACS code chain for when a vehicle was 

passive in an accident and had no opportunity to take any action to avoid the 

accident – for example a vehicle being struck from behind while stationary or being 

hit by a vehicle suddenly entering its lane. The standard chains are defined as: A1- 

C1-J1-J1.4 and A1-C1-J2-J2.4. 

During the practical work of individual case analysis some suggestions for 

improvements were put forward and SNACS 1.2 was therefore revised. The revision 

resulted in DREAM 3.0 (Wallén Warner et al. 2008) which is the latest version of the 

method recommended to be used for further individual case analyses. Fagerlind et al. 

(2008) describes the methodology development process in more detail. 


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3 Aggregated SNACS-data analysis 

Unlike the traditional approach where accident causes are classified into individual 

contributing factors, SNACS charts show the causal relationships between the 

factors. Individually coded factors can be analysed and aggregated in for example 

bar charts diagrams. By aggregating SNACS charts, common causation patterns 

may be identified among several charts. However, when large numbers of charts are 

selected for aggregation the details may not be so evident. The purpose of 

aggregation depends on the current research question. 

The main approach when analysing the SafetyNet Accident Causation Data was to 

use an analysis based on context and vehicle trajectory. Since an accident can 

contain more than one trajectory (i.e. one trajectory per involved vehicle), the sorting 

has been done on a vehicle level. The trajectory-based approach is chosen because 

it is the type of sorting which gives the closest coupling to existing crash databases if 

further comparison between this in-depth material and a broader material on a more 

statistical level needs to be carried out. 

Prior to sorting the vehicles according to trajectory, all accidents involving Slower 

moving Vulnerable Road Users (SVRU) (i.e. pedestrians and bicyclists) and their 

counterpart, were sorted into a separate group for separate treatment. The reason for 

this choice is that it was believed that accidents involving SVRU would have different 

causation patterns and characteristics, compared to single or multiple motorised 

vehicle crashes. 

The sorting resulted in three main trajectory based groups of accidents and one 

group of accidents with SVRU (centre responsible for analysis in brackets): 

• Vehicle leaving its lane (VSRC) 

• Vehicle encountering something in its lane, either in front or from the rear 

(DITS) 

• Vehicle encountering another vehicle on crossing paths (Chalmers) 

• Accidents involving SVRU (VALT). 

Each main group can be further divided into subgroups relating to conflict scenario, 

participant or counterpart, for further analysis. The subgroups for each main group 

are described in more detail under each section. 

Note that since the SafetyNet analysis is causation focused rather than outcome 

focused, the subgroups under each section may not seem completely intuitive, since 

they do not follow the traditional outcome categorisation from passive safety. 

However, the subgroups suggested here are ones which are hypothesised to present 

the clearest differences in causation patterns within each of the three main trajectory 

based groups. 


Page 19


In this study the aggregation was done without considering the levels of confidence 

for each causal chain. This means that in the final aggregation, a low confidence 

causal chain is attributed the same importance as a causal links with a high level of 

confidence. 

The context variables were chosen so a comparison can be made with other 

European databases, for example CARE (Community database on Accidents on the 

Roads in Europe). The variables are listed below, values inside brackets: 

- Speed limit, posted speed interval – roughly matching urban, mixed and rural 

area (90 kph) 

- Horizontal alignment for vehicle leaving its lane (straight, bend to left, bend to 

right) 

- Time of day (00:00-06:00, 06:00-12100, 12:00-18:00; 18:00-24:00) 

- Age (


All critical events and causes from the case charts were summarised and an 

aggregated SNACS analysis chart was created. All critical events and causes as well 

as the links between them were counted once for every vehicle involved. 

Since the charts often got too complicated the charts were divided into two parts. . 

The first part includes the critical events as well as all the first level causal factors, 

illustrated in Figure 11. 

Figure 11, SNACS-chart-critical event and first level cause 

The second part includes all contributing factors and in the box of the first causal 

factor the number of linked critical events is presented, see Figure 12. 


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Figure 12, SNACS-chart-all causes 

In the charts, the link frequency is illustrated by different line thicknesses. 

3.1 Vehicle leaving its lane 

When superimposing the SNACS charts in the selected group, common causation 

patterns may be identified among several charts. However, when large numbers of 

charts are selected for aggregation the details may not be so evident. In this study 

the aggregation was done without considering the levels of confidence for each 

causal chain. This means that in the final aggregation, a low confidence causal chain 

is attributed the same importance as a causal links with a high level of confidence. 

An accident starting with a vehicle-leaving-lane trajectory is defined as a crash which 

is initiated when a vehicle leaves its lane, by crossing the lane boundary either to the 

left or the right. Typical outcomes in these types of accidents (see Figure 13) is; 

colliding with a vehicle travelling in the opposite direction either as a result of 

overtaking another vehicle (1a) or drifting across the median line (2a); lane change 

crashes where there is a collision with a vehicle travelling in the same direction (1b) 

or road departures (2b-2c). 


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1a 1b 2a 2b 2c 

Figure 13, Leaving lane accident scenarios 

Vehicles are not included in the leaving lane category if they first collide with a 

vehicle or an object in its own lane and then exit the lane – these accidents will 

belong either to the main groups ‘vehicle encountering something while remaining in 

its lane’ or ‘vehicle encountering another vehicle on crossing paths’, depending on 

where the initial crash scenario fits. 

As stated above, since the analysis is causation focused rather than outcome 

focused, subdividing this accident group according to outcomes is not a viable 

strategy. Instead accidents belonging to the ‘leaving lane’ category can be divided 

into two subgroups which, it is hypothesized, reflect two different types of causation 

patterns. 

A vehicle may leave its lane either intentional (e.g. driver actively changing lane or 

initiating an overtaking of another vehicle) or unintentional (driver drifting out of lane 

or losing control). Therefore the ‘leaving lane’ category can be divided into two 

groups ‘Intentional’ and ‘Unintentional’. This results in the following subgroups: 

A vehicle leaves its lane intentionally 

1. A vehicle leaves its lane by crossing the median line intentionally (i.e. starts 

to overtake another vehicle) 

2. A vehicle leaves its lane by intentionally crossing a lane marker (i.e. 

initiating a lane change manoeuvre) but does not cross the median line 

A vehicle leaves its lane unintentionally 

All other lane departures were the initial crossing of a lane marker or median line is 

unintentional and the vehicle: 

3. Crosses the median line due to losing control over the vehicle 

4. A vehicle leaves its lane due to losing control over the vehicle without 

crossing the median line 

The ‘intentional’ and ‘unintentional’ groups can be further divided according to 

whether or not the vehicle has crossed the median line, as shown above. In 

subcategories 1 and 3 the vehicle has crossed over to the opposite carriageway and 

in subcategories 2 and 4 the vehicle has remained on its own side of the road. This 

allows outcome related causation patterns to be identified. 


Page 23


3.1.1 Sorting 

The accidents where a vehicle leaves its lane were initially selected by using the 

GDV-codes, see Appendix B. This subset of cases then required manual checking as 

some cases included a “leaving lane” vehicle and a vehicle which had encountered 

something while remaining in its own lane and was assigned to the “own lane” 

category. For example, when one vehicle crossed the median and collided with an 

oncoming vehicle, the oncoming vehicle was assigned to “own lane” category. 

Vehicles which had left their lane in order to avoid an object in their own lane were 

also assigned to the “own lane” category. 

The Accident Summary and the Event detail (1-6) variables and its values, such as 

‘Ran off road – nearside’, were investigated for each vehicle. A condition was that the 

collision had to have occurred following an event or an action described in the 

accident summary, which indicated that the vehicle had left its lane. The ‘crossed 

median/centre line’ and ‘Ran off road’ events are examples of such an event. It was 

also double-checked that there were no accidents involving a pedestrian or bicycle in 

the sample. Then each vehicle was assigned to the relevant subgroup, i.e. intentional 

and unintentional lane departures. 

If a vehicle has left its lane ‘intentionally’ the driver has made a deliberate choice to 

change lanes either to overtake on a multiple carriageway road or a planned 

overtake of a obstacle or slower moving vehicle. Unintentional is when a vehicle has 

left its lane due to some kind of loss of control. If the vehicle had an associated ‘loss 

of control’ GDV-code it was classed as 'Unintentional' except where the 'Driver 

Manoeuvre’ variable suggests a deliberate action, e.g. 'overtaking' or 'changing lane'. 

Vehicles with all other GDV codes had to be checked manually using the accident 

summary, driver manoeuvre and events codes. 

Both the ‘intentional’ and ‘unintentional’ groups were subdivided according to whether 

or not the vehicle had crossed the median line. If the vehicle had crossed the median 

line then it was assigned to the ‘opposite’ subgroup as the vehicle had entered the 

path of vehicles travelling in the opposite direction. Those which had not crossed the 

median line were assigned to the ‘same’ subgroup because if they encountered 

another vehicle it would be travelling in the same direction. The same method was 

used for both groups so the following description does not distinguish between the 

‘intentional opposite/same’ and ‘unintentional opposite/same’ subcategories. Vehicles 

were assigned to the ‘opposite’ subgroup if they had a ‘cross median/central line’ 

and/or a ‘left the road – offside’ event, with the exception of one-way streets. In the 

latter case the vehicle would be assigned to the ‘same’ category. Vehicles which had 

the event ‘left the road – nearside’ were also assigned to the ‘same’ category. All 

remaining vehicles were assigned to the correct category manually by examining the 

accident summary and event variables. 

3.1.2 Analysis and results 

In total 354 vehicles in the SafetyNet Accident Causation Database were identified as 

having a leaving lane trajectory and had complete SNACS codes (see Table 9). 6 

vehicles have been excluded from analysis as they had incomplete SNACS codes 

and 1 vehicle which was involved in a parking related accident was excluded as it 


Page 24


was thought that the accident did not fit with the leaving lane definition. It should be 

remembered that the results presented in this section deal only with vehicles that 

have been labelled as ‘leaving lane’ and do not include vehicles which may have 

been involved in the same accident but were assigned to a different trajectory 

category. 

Table 9, Number of vehicles per leaving lane category 

Category 

All leaving lane 354 

Leaving lane unintentionally 305 

same 168 

opposite 137 

Leaving lane intentionally 49 

same 19 

opposite 30 

Number of vehicles with SNACS 

SNACS chart have been created for both the leaving lane vehicles and the subcategories. 

This results section will firstly present some overall characteristics of the 

leaving lane cases as a whole before presenting the SNACS diagrams and 

describing which critical events and causes appear most frequently. Comparisons will 

then be made between the SNACS chart for the subgroups. However, as shown in 

Table 9, there were very few vehicles in the leaving lane intentionally subcategory 

when compared to the leaving lane unintentional subcategory therefore meaningful 

comparisons can only be made within rather than between these subcategories. 

General characteristics of leaving lane vehicle accidents 

In total, 354 vehicles were assigned to the leaving lane category. Figure 14 shows 

the distribution of vehicle types and the number of vehicles involved in the accident 

as a whole for leaving lane vehicles. Over ¾ of the leaving lane vehicles were cars 

(77%), 10% were trucks and 8% were motorcycles or mopeds. The majority of 

accidents involving a vehicle classified as leaving its lane were single vehicle 

accidents (67%) with the next largest share being 2 vehicle accidents (27%). 


Page 25


Vehicle Types 

Number of vehicles involved in accident 

34, 10% 

1, 0% 

27, 8% 

14, 4% 4, 1% 

Bus / Minibus 

Car / MPV 

Motorcycle / Moped 

Other 

Truck 

Van 

97, 27% 

2, 1% 

18, 5% 

237, 67% 

Single vehicle 

2 vehicle 

3 vehicle 

4 vehicle 

274, 77% 

Figure 14, Vehicle types and the number of vehicles involved in the accident for leaving lane 

vehicles 

The distribution of vehicle types as shown in Figure 14 is roughly comparable to the 

SafetyNet accident causation database as a whole however single vehicle accidents 

are over represented in the leaving lane category when compared to the whole 

dataset. Single vehicle accidents are most often associated with the rural setting so it 

is unsurprising that the local area ‘rural’ has a larger share (59%) than ‘urban’ or 

‘mixed’, as shown in Figure 15. 

Average speed limits and actual speed 

by local area 

Average Speed Limit 

Average Actual speed 

Speed in Km/h 

90 

80 

70 

60 

50 

40 

30 

20 

10 

0 

Urban Mixed Rural 

210, 

59% 

Local Area 

103, 

29% 

41, 12% 

Urban 

Mixed 

Rural 

Local Area 

Figure 15, Local area and average speed limits and actual speeds for leaving lane vehicles 

It can be conjectured that speed limits and actual traffic speeds in rural settings will 

be greater than in urban areas and be somewhere in between in mixed areas. Figure 

15 shows that this is generally true for leaving lane vehicles. Average pre impact 

speeds and the road speed limits are higher for rural areas (84/82 km/h) than for 

urban areas (61/52 km/h). 


Page 26


Figure 16 demonstrates that the age of drivers of leaving lane vehicles is skewed 

towards the younger age groups. The age category with the most drivers is the under 

25 category with 120 drivers, closely followed by the 25-44 category with 111 drivers. 

Age of drivers 

140 

120 

Number of drivers 

100 

80 

60 

40 

20 

0 


In summary the characteristics of leaving lane vehicles which stand out are that they 

tend to be involved in single vehicle accidents, they occur more often on rural roads 

and are more likely to be driven by a younger driver. 

Leaving Lane SNACS analysis 

The critical events that dominate in the leaving lane cases are ‘Speed (A5)’ and 

‘Direction’ (A6). Figure 18 shows that 46% of leaving lane vehicles had the critical 

event of ‘Direction’ and 30% had the critical event of ‘speed’. 

Leaving Lane - critical events 

(354 vehicles; 354 critical events) 

1% 

1% 

12% 

A1 

A2 

Timing 

Duration 

6% 1% 

3% 

A3 

Force/(power) 

A4 

Distance 

46% 

A5 

A6 

Speed 

Direction 

A7 

Object 

30% 

A8 

Sequence 

Figure 18, Distribution of critical events for all leaving lane cases 

The 3rd most frequent critical event was ‘Force’ (A3) with the other critical events 

being coded relatively rarely. Each critical event in a SNACS chain is followed by a 

‘specific critical event’ which provides more detail. Figure 19, Distribution of specific 

critical events in all leaving lane cases shows the distribution of specific critical 

events. 


Page 28


Leaving lane - specific critical events 

(354 vehicles; 354 critical events) 

46% 

1% 

1% 

1% 

3% 1% 

1% 

0% 

12% 

1% 

3% 

A1.1 Premature action 

A1.2 Late action 

A1.3 No action 

A2.1 Prolonged action/movement 

A3.1 Insufficient force 

A3.2 Surplus force 

A4.1 Prolonged distance 

A4.2 Shortened distance 

1% 

29% 

A5.1 Surplus speed 

A5.2 Insufficient speed 

A6.1 Incorrect direction 

A7.1 Adjacent object 

A8.4 Extraneous action 

Figure 19, Distribution of specific critical events in all leaving lane cases 

There is only 1 specific critical event available to be code for ‘Direction’ so all of these 

leaving lane vehicles have the specific critical event of ‘Incorrect direction’ (A6.1). 

Figure 19 also shows that ‘Surplus speed’ (A5.1) and ‘Surplus force’ (A3.2) are the 

dominant specific critical events for ‘Speed’ and ‘Force’ respectively. 

Links between critical events and causes are displayed in SNACS charts. Two 

SNACS charts were created for the leaving lane cases as a whole. The first, Figure 

20, shows the links between the critical event and the 1st level cause in the SNACS 

chain. The second, Figure 22, displays the links between all the causes coded. 


Page 29


Figure 20, Leaving lane critical events link cause SNACS chart 


Page 30


Figure 20 shows that the most commonly occurring links between the critical event 

and first level cause for leaving lane vehicles is ‘Direction’ to ‘Inadequate plan’ (A6- 

D1) and ‘Speed’ to ‘Inadequate plan’ (A5-D1). This makes ‘Inadequate plan’ (D1) the 

most commonly occurring first level cause for the leaving lane vehicles with a 35% 

share as shown in Figure 21. The second most common 1st level cause is 

‘Observation missed’ (B1) with 18%. ‘Observation missed’ (B1) is linked most 

frequently with ‘Direction’ (A6) and the A6-B1 link occurs 57 times. 

0% 

5% 

0% 

2% 

1% 

0% 

7% 

1% 

Leaving lane - 1st cause 

(354 vehicles; 525 causes) 

10% 18% 

35% 

1% 

16% 

0% 

4% 

B1 

B3 

C1 

C2 

C3 

D1 

D2 

E2 

E3 

E5 

E6 

F1 

I1 

J1 

J2 

Observation missed 

Wrong identification 

Faulty diagnosis 

Wrong reasoning 

Decision error 

Inadequate plan 

Priority error 

Fear 

Distraction 

Performance Variability 

Inattention 

Functional impairment 

Equipment failure 

Communication failure 

Information failure 

Figure 21, Distribution of 1st level cause in all leaving lane cases 

Figure 21 also shows that ‘Faulty diagnosis’ (C1) occurs relatively frequently as a 1st 

level cause (16%) and in Figure 20 it can be seen that ‘Faulty diagnosis’ (C1) has 

fairly strong links with the critical events ‘Speed’ (A5-C1), ‘Direction’ (A6-C1) and 

‘Force’ (A3-C1) with 36, 24 and 16 links respectively. Although ‘Faulty diagnosis’ 

occurs most frequently as a first level cause, it also appears as a second cause with 

9 links with ‘Observation missed’ (B1-C1), as show in Figure 22 


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Figure 22, Leaving lane causes SNACS chart 

Figure 22, displays all the cause to cause links that occur in the leaving lane cases. 

Although complex, this diagram allows the most commonly occurring cause chains to 

be identified as the greater the number of links between 2 causes, the greater the 

thickness of the linking lines. The number of links between a cause and a critical 

event appears on the diagram in blue writing following the prefix CEL (Critical Event 

Link). 

The most common links between causes for the leaving lane vehicles are between 

‘Inadequate plan’ and ‘Insufficient knowledge’ (D1-L2, 43 links) and ‘Inadequate plan’ 

and ‘Influence of substances’ (D1-E7, 42 links). Figure 23 shows that ‘Influence of 

substances’ (E7) is the most commonly occurring second cause in the SNACS chain 

with a share of 16%. Both the causes SNACS chart and second cause pie chart 

show that ‘Fatigue’ (E4) is potentially an important second cause with a share of 12% 

and strong links with both ‘Observation missed’ (B1-F4, 28 links) and ‘Inadequate 

plan’ (D1-F4, 24 links). ‘Information failure’ (J2) also appears to be an important 

cause as it accounts for 10% of the first level causes, 6% of the second and also has 

strong links with ‘Faulty diagnosis’ (C1-J2, 25 links) and ‘State of road’ (J2-K5, 37 

links). 


Page 32


1% 

1% 

1% 

2% 

10% 

5% 

2% 

2% 

6% 

4% 

1% 

2% 

5% 

9% 

Leaving Lane - 2nd Cause 

( 285 vehicles; 425 causes) 

3% 

3% 

2% 

1% 

8% 

16% 

12% 

2% 

C1 Faulty diagnosis 

D1 Inadequate plan 

E2 Fear 

E3 Distraction 

E4 Fatigue 

E6 Inattention 

E7 Influence of substances 

E8 Physiological stress 

E9 Psychological stress 

F1 Functional impairment 

F2 Cognitive bias 

I1 Equipment failure 

J2 Information failure 

K1 Maintenance failure - vehicle 

K2 Maintenance failure - road 

K5 State of road 

L2 Insufficient knowledge 

N1 Inadequate road design 

N2 Permanent obstruction to view 

N4 Temporary obstruction to view 

O1 Unpredictable system functions 

Other < 5 links 

Figure 23, Distribution of 2nd level cause in SNACS chain 

Each leaving lane vehicle has a SNACS analysis chart made up of 1 or more SNACS 

chains. The majority of leaving lane vehicles have at least 1 chain made up of a 

critical event and two causes and therefore are included in Figure 23. Only 52 

vehicles have a SNACS chain of more than 2 causes, as shown in Table 10, 

therefore further analysis of these have not been made. 

Table 10, Maximum chain lengths for leaving lane vehicles 

Maximum Chain Length 

Number of vehicles 

(Critical event plus causes) 

2 69 

3 233 

4 48 

5 4 

Description 

Critical event, 1st level 

cause 


cause, 2nd level cause 


cause, 2nd level cause, 3rd 

level cause 


cause, 2nd level cause, 3rd 

level cause, 4th level cause 

The following tables display the most common critical events, critical event to 1st 

level cause links and cause to cause links for different groups of leaving lane 

vehicles. In Table 11 vehicles have been grouped according to drivers’ age and 

Table 12 shows the results for horizontal alignment of the roadway. 


Page 33


Table 11, Most frequent SNACS links for leaving lane vehicles according to driver age 

Age group 


to cause links also differ between the age groups. For example the frequency which 

‘Fatigue’ occurs differs as follows. ‘Observation missed’ to ‘Fatigue’ (B1-F4) is the 

most common cause to cause link for the 45-64 age group and ‘Inadequate plan’ to 

‘Fatigue’ (D1-E4) is the third most common link for the 25-44 age group. ‘Fatigue’ 

does not appear in the 3 most common cause to cause links for either the under 25 

or 65+ age groups. 

Table 12, Most frequent SNACS links for leaving lane vehicles according to horizontal 

alignment of roadway 

Horizontal Alignment of Roadway 

Bend (n = 207) Straight (n = 142) 

Critical A6 Distance 85 A6 Distance 74 

Events A5 Speed 77 A5 Speed 30 

A5D1 

Speed - 

51 A6D1 

Distance - 

31 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 

A6D1 

A6B1 

D1E7 

D1L2 

J2K5 


Distance - 

Inadequate plan 39 A6B1 

Distance - 

Observation 

missed 


- Influence of 

substances 


- Insufficient 

knowledge 

Information 

Failure - State 

of road 

33 A5D1 

34 D1E4 

30 B1E4 

27 C1J2 


Distance - 

Observation 

missed 

22 

Speed - 

Inadequate plan 20 

Inadequate plan - 

Fatigue 13 

Observation 

missed - Fatigue 12 

Faulty diagnosis - 

Information failure 12 

Again, as shown in Table 12, ‘Distance’ (A6) and ‘Speed’ (A5) dominated the critical 

events. There is some variation between the ‘bend’ and ‘straight’ groups’ critical 

event to 1st level cause links. For example, the link ‘Speed’ to ‘Inadequate plan’ (A5- 

D1) was the most commonly occurring critical event to 1st level cause link for ‘bend’ 

but only the third most common for ‘straight’. ‘Fatigue’ is potentially an important 

cause when a vehicle leaves the lane on a straight road, as it appears in 2 of the 

most frequently occurring cause to cause links, but does not feature in the top 3 

equivalent links for ‘bends’. 

Leaving Lane Sub-categories SNACS analysis 

As shown in Table 9 there were very few vehicles in the leaving lane intentionally 

subcategory when compared to the leaving lane unintentional subcategory therefore 

meaningful comparisons can only be made within rather than between these 

subcategories. 


Page 35


Leaving Lane unintentionally 

305 vehicles were classified as having left their lane unintentionally. Because this 

represents 86% of all the leaving lane vehicles the resulting SNACS analysis charts 

are very similar to the equivalent for all leaving lane vehicles. These diagrams are 

therefore not represented here. As unintentionally leaving a lane occurs when a 

vehicle becomes out of control, the resulting events are likely to be fairly random – for 

example whether the vehicle leaves the road to the offside or hits an oncoming 

vehicle. However it was hypothesised that there may be a difference in the causation 

patterns between vehicles who leave their lane or the road without crossing the 

median line (same) and those which do (opposite). 137 vehicles had crossed the 

median line and 168 had not. These sub groups are similar in number therefore it is 

possible to make direct comparisons between them. Figure 24 and, Figure 25 show 

the cause to cause links for the leaving lane unintentionally ‘opposite’ group and 

leaving lane unintentionally ‘same’ group respectively. 

Figure 24, Leaving lane unintentionally – opposite – causes SNACS chart 


Page 36


Figure 25, Leaving lane unintentionally – same – causes SNACS chart 

The causation patterns between the leaving lane unintentionally vehicles that did 

cross the median line (opposite) and those that did not (same) are broadly the same. 

However there are a few notable differences. The link ‘Observation missed’ to 

‘Fatigue’ (B1-E4) appears stronger for the vehicles that crossed the median line (20 

links; Figure 24) than for those that did not (8 links; Figure 25). Conversely the links 

‘Inadequate plan’ to ‘Psychological stress’ (D1-E9) and ‘Inadequate plan’ to 

‘Insufficient knowledge’ (D1-L2) are stronger for the vehicles that did not cross the 

median line (Figure 25). 

Whether or not the vehicle crosses the median line appears to be associated with the 

age of the driver. Figure 26 shows that the distribution of driver age for vehicles that 

cross onto the opposite carriageway is skewed towards the younger age groups. In 

contrast, vehicles that do not cross the median line are slightly more likely to be 

driven by a driver in the 25-44 age group than the under 25s. 


Page 37


Age of Drivers 

70 

60 

Number of Drivers 

50 

40 

30 

20 

10 

opposite 

same 

0 


Figure 27 shows that the critical events ‘Timing’ (A1) and ‘Speed’ (A5) occur more 

frequently. ‘Timing’ with a share of 27% appears to be particularly associated with 

leaving lane intentionally vehicles when compared with its share for the whole of the 

leaving lane vehicles (6%, see Figure 18). The link ‘Timing’ to ‘Observations missed’ 

(A1-B1, 9 links) is also over represented with over half of the A1-B1 links that occur 

for leaving lane vehicles as a whole (14 links, see Figure 20). Given that leaving lane 

intentionally vehicles are usually engaged in an overtake manoeuvre, it is perhaps 

unsurprising that ‘Timing’ (A1) features strongly along with ‘Observation missed’ (B1). 

Generally, as shown in Figure 28, the cause to cause links for leaving lane 

intentionally vehicles follow the pattern of cause to cause links for leaving lane cases 

as a whole. For example the most frequently occurring link for leaving lane 

intentionally vehicles are between ‘Inadequate plan’ and ‘Insufficient knowledge’ (D1- 

L2) which was the most frequently occurring link for all leaving lane vehicles. 

Figure 28, Leaving lane intentionally causes SNACS chart 

Figure 28 also shows a slight overrepresentation of the links ‘Observation Missed’ to 

Inadequate Plan’ (B1-D1) and ‘Observation Missed’ to ‘Faulty diagnosis’ (B1-C1), 

when compared to the leaving lane accidents as a whole. Again these would be 

expected characteristics of accidents involving overtake manoeuvres. 


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3.1.3 Discussion and conclusions 

354 vehicles were assigned to the leaving lane trajectory category. 86% of these 

were classed as having left their lane unintentionally due to loss of control and 14% 

were classified as having left their lane intentionally as part of a lane change or 

overtake manoeuvre. 

77% of the leaving lane vehicles were cars and single vehicle accidents accounted 

for 67% of the accidents involving a leaving lane vehicle. Most leaving lane vehicles 

had an accident in a rural setting (59%) and the distribution of driver age for leaving 

lane vehicles was skewed towards the younger drivers with a third of drivers being 

under the age of 25. 

The most frequently occurring critical events for leaving lane accidents were 

‘Direction’ (A6) and ‘Speed’ (A5). Travelling too fast leading to a loss of control or 

travelling in the wrong direction would be expected for leaving lane accident and are 

also associated with single vehicle accidents occurring on a rural road. The fact that 

these characteristics are prevalent in the leaving lane cases lends validity to these 

findings. 

Common cause to cause links in leaving lane accidents were ‘Inadequate plan’ to 

‘Insufficient knowledge’ and ‘Influence of substances’ (D1-L2; D1-E7) however these 

links only occurred together in 5 leaving lane vehicles. The link chain A5-D1-L2 

occurs 21 times suggesting that this scenario is a fairly common one for leaving lane 

vehicles. An example of this scenario would be if a vehicle was travelling on a road 

which was unfamiliar to the driver (L2) which led to the driver not anticipating a bend 

(D1) which in turn led to the driver travelling too fast (A5). When the leaving lane 

vehicles were grouped according to the horizontal alignment of the road on which 

they were travelling, the link ‘Speed’ to ‘Inadequate plan’ (A5-D1) was the most 

commonly occurring critical event to 1st level cause link for ‘bend’ but only the third 

most common for ‘straight’. 

‘Inadequate plan’ (D1) is the most commonly occurring first level cause for the 

leaving lane vehicles with a 35% share. The second most common 1st level cause is 

‘Observation missed’ (B1) with 18%. ‘Observation missed’ (B1) is linked most 

frequently with ‘Direction’ (A6) and the A6-B1 link occurs 57 times. ‘Faulty diagnosis’ 

(C1) also occurs relatively frequently as 1st levels cause (16%) and has strong links 

with ‘Information failure’ (C1-J2, 25 links). 

Another link chain that is highlighted by the leaving lane cause to cause SNACS 

chart is ‘Faulty diagnosis’ to ‘Information failure’ to ‘State of road’ (C1-J2-K5). An 

example of this would be where there is oil on the road (K5) but the driver does not 

see it (J2) which leads to the driver assuming that the road surface is free of 

contaminates (C1). 

‘Fatigue’ (F4) potentially is an important cause with strong links with ‘Observation 

missed’ (B1-F4) and ‘Inadequate plan’ (D1-E4). However the prevalence of this 

cause differs according to how the leaving lane vehicles are grouped. When grouped 

by age, ‘Observation missed’ – ‘Fatigue’ (B1-F4) is the most common cause to cause 

link for the 45-64 age group and ‘Inadequate plan’ to ‘Fatigue’ (D1-E4) is the third 


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most common link for the 25-44 age group. ‘Fatigue’ does not appear in the 3 most 

common cause to cause links for either the under 25 or 65+ age groups. Within the 

leave lane unintentionally cases, the link ‘Observation missed’ to ‘Fatigue’ (B1-E4) 

appears stronger for the vehicles that crossed the median line (20 links) than for 

those that did not (8 links). 

Due to the large numbers of leaving lane unintentionally vehicles the SNACS chart 

diagrams for this group mirror the results for the whole of the leaving lane vehicles. 

Conversely, the small number of leaving lane intentionally vehicles makes drawing 

substantial conclusions about the causation patterns in this group problematic 

although some variation when compared to the leaving lane vehicles as a whole was 

identified. 

The SNACS chart diagrams for vehicles assigned to the leaving lane trajectory reveal 

that there are many causes or factors that contribute to leaving lane accidents. They 

suggest that human factors such as ‘Influence of substances’, ‘Insufficient 

knowledge’ and ‘Fatigue’ and environmental issues such as the ‘State of road’ (K5) 

can lead to cognitive errors such as ‘Faulty diagnosis’ and ‘Inadequate plan’ and 

contribute to critical events such as travelling in the wrong direction (Direction A6) or 

travelling too fast (Speed A5). 

The aim of this analysis is not to explore and evaluate the effectiveness of new 

technologies that are designed to assist in preventing leaving lane accidents such as 

lane departure warning; brake assist or electronic stability control in regards to 

collision mitigation, but rather demonstrate the potential uses for the accident 

causation database and identifying common accident scenarios and areas of interest 

or future work. 

3.2 Vehicle encountering something in its lane, either in front 

or from the rear 

When superimposing the SNACS charts in the selected group, common causation 

patterns may be identified among several charts. However, when large number of 

charts are selected for aggregation the details may not be so evident. In this study 

the aggregation was done without considering the levels of confidence for each 

causal chain. This means that in the final aggregation, a low confidence causal chain 

is attributed the same importance as a causal links with a high level of confidence. 

This trajectory group represents vehicles encountering something in its own lane 

which typically result in a front or rear end collision for the subject vehicle. The main 

group is divided into four subgroups, depending on the type of conflict with another 

vehicle, an animal or an object. 

To make a complete analysis possible the group was divided in four subgroups 

according to the position of the vehicle at the moment of the accident. The subgroups 

are: 

Being struck from behind – RF (Figure 29, scenario 1) 

Subject vehicle is struck from behind by another vehicle. 


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In this subgroup the vehicles inserted have as first collision event type “rear-front”. 

Striking vehicle in front – FR (Figure 29, scenario 2) 

Subject vehicle strikes a vehicle in front of it in its own lane and travelling in the same 

direction as the subject vehicle. This subgroup could be further divided into two subscenarios: 

• 2a. The struck vehicle is braking hard prior to the crash 

• 2b. The struck vehicle is moving slowly or has stopped prior to the crash (for 

example, because of slow-moving traffic ahead). 

In this subgroup the vehicles inserted have as first collision event type “front-rear”. 

Being struck by a vehicle which has left its lane – S (Figure 29, scenario 3) 

Subject vehicle is struck by an oncoming vehicle frontally or from the side by a 

vehicle which has left its lane. 

This subgroup does not include vehicles which have “rear-front” or “front-rear” as 

first collision event type. 

Striking object other than vehicle in front – O (Figure 29, scenarios 4a and 4b) 

Subject vehicle strikes an animal (wild or domesticated) or another object in front that 

is fixed or not fixed. 

In this subgroup the vehicles have as first collision event type “Collision with object 

not fixed”. 

1 2 3 4a 4b 

Figure 29, Vehicle encountering something while remaining in its lane scenarios (1-5), subject 

vehicle is gray. 

3.2.1 Sorting 

The accidents where a vehicle encountering something in its lane (either in front or 

from the rear) were initially selected by using the GDV-codes, see Appendix B. This 

subset of cases then required manual checking as some cases included an “own 

lane” vehicle and a vehicle that had left it lane which was assigned to the “leaving 

lane” category. 

Concerning the SafetyNet sample this group is composed of 537 vehicles and 763 

specific causes. Table 13 reports the figures of the different subgroups. It is possible 

to see that subgroup S is the biggest with 217 vehicles and 294 chains. In contrast 

subgroup O is the smallest with only 10 vehicles and 16 chains. 


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Table 13, Vehicles distribution among the subgroups 

Vehicles 

Chains 

Being struck from behind (RF) 161 209 

Being struck by a vehicle which has left its lane (S) 217 294 

Striking vehicle in front (FR) 149 244 

Striking object other than vehicle in front (O) 10 16 

Total 537 763 

3.2.2 Analysis 

The analysis was performed at subgroup level and three different analyses were 

made. Concerning subgroup O the analysis was not performed because only 10 

vehicles are in this subgroup. 

For the other three subgroups aggregate analyses on context variables and SNACS 

results were performed. Concerning the SNACS, the critical events distribution, the 

first level causes distribution, the last general causes distribution and the specific 

causes distribution were analysed. The analysis also contains relation charts of 

critical events to first level causes and between general causes. 

Figure 30 reports the vehicle type distribution. 71% of the vehicles are Car/MPV 

followed by Truck - only 10% of the total vehicles, Van, 8% and motorcycles/mopeds 

7%. 

Figure 30, Vehicle type distribution 


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Several variables have been analysed in each subgroup to understand relations 

between causal factors and context variables. The following variables have been 

analysed in each subgroup: 

• Time, day of the week and month; 

• Driver gender; 

• Driver age; 

• Accident local area; 

• Traffic flow; 

• Speed limit and pre-impact speed. 

In the following sections the analysis for each subgroup is reported. 

3.2.3 Results 

Results from Being struck from behind (RF) 

In the RF sub group there are 161 vehicles and a total of 209 chains. The distribution 

of critical events is reported in Figure 31, the distribution of the first level causes is 

reported in Figure 33, the distribution of the last general causes is reported in Figure 

32 and the specific cause’s distribution is reported in Figure 34. 

Figure 31, Critical event in RF subgroup 

(For explanation of cause codes, see 

Appedix A) 

Figure 32, Last general causes in RF 

subgroup chains (For explanation of cause 

codes, see Appedix A) 


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Figure 33, First level causes in RF 



Figure 34, Specific causes in RF subgroup 

chains (For explanation of cause codes, 

see Appedix A) 

The four figures reported above show that the standard chain (The standard chains 

are defined as: AX-C1-J1 or J2-J1.4 or J2.4) has often been used in this subgroup. 

This is normal because this subgroup includes vehicles that were struck from behind 

and in this case the main driver problem is that the driver did not understand what 

was going because of a missing communication with the other drivers (J1) or with the 

road environment (J2). 

Looking at Figure 34 except for J1.4 and J2.4 the most important causes are C1.1 

(Error in mental model) and D1.2 (overlooked side effect). Both of them are related, 

as is the standard chain, to a driver’s missing comprehension of the situation. 

The RF relation charts between critical events and first level causes and between the 

causes are reported in Figure 35 and Figure 36. 


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Figure 35, RF relation chart between critical events and first level causes 


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Figure 36, RF relation chart between causes 


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Figure 35 shows a very strong relationship between A1 and C1 (60 links), A3 and C1 

(13 links) and A3 and D1 (11 links). A1-C1 links are related to the high number of 

standard chains as is probably for A3-C1 as well. The high number of A3-D1 links 

probably is due to inadequate planning of the manoeuvre. 

Figure 36 shows as expected a very strong relation between C1 and J1 and C1 and 

J2. No other relevant links are observed in the chart. 

The context variables analyses are reported from Figure 37 to Figure 44. 

Figure 37, Accidents by day of the week 

161 vehicles 161 vehicles 

Figure 38, Accidents by month of the year 

Figure 39, Accident by time of the day 


Figure 40, Drivers gender 

Figure 41, Drivers age 


Figure 42, Accident local area 


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Figure 43, Traffic flow 

161 vehicles 

161 vehicles 

Figure 44, Average speed limits and 

average pre-impact speeds distribution. 

Concerning when the accidents occurs it is possible to say that most of the accidents 

collected happened on Wednesday (20%), Friday and Thursday (both 18%). Most of 

the accidents happened in May (13%) and in August (11%) and in the afternoon 

(55%) or in the morning (33%). 

Concerning drivers’ gender, a large majority are male (65%) and the driver age 

distribution show that the large majority of the drivers (49%) are in the age category 

25-49 years. 30% of the drivers are in the 45-65 years category. Drivers younger 

than 25 years old are 12% of the sample and only 7% of the drivers are 65 years old 

or older. 

Regarding the accident local area, the majority of the accidents happen in urban 

areas (59%), 33% of the accidents happen in rural areas and only 8% happen in a 

mixed area. The traffic flow is normal in 50% of the accidents, heavy in 32% and light 

for only 17% of the accidents. 

Regarding the average speed limits and the average pre-impact speeds the results 

reported above show that the average speed limit is high in the rural areas - about 90 

kph, low in mixed areas, about 80 kph, and very low in urban areas - only 54 kph. 

Concerning average pre-impact speeds, they are much lower than the average 

speed limit ones. Indeed in rural areas the average pre-impact speed is about 22 

kph, urban is about 12 kph and mixed is about 6 kph. 

Striking vehicle in front (FR) 

In FR subgroup there are 149 vehicles for a total of 244 chains. The distribution of 

critical events is reported in Figure 45, the distribution of the first level causes is 


47 and the specific cause distribution is reported in Figure 48. 


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Figure 45, Critical event in FR subgroup 



- 

149 critical events 209 first level 

Figure 46, First level causes in FR 



244 specific causes 

242 last general causes 

Figure 47, Last general causes in FR 



Figure 48, Specific causes in FR subgroup 



A1 (timing) is the most used critical event followed by A4 (distance) and A5 (speed). 

The most used first level cause is B1 (observation missed), followed by C1 (faulty 

diagnosis) and D1 (inadequate plan). C1, E3 (Distraction), D1 and E6 (inattention) 

are the most used last general causes. 

Concerning the specific causes, the most used is C1.1 (error in mental model), 

followed by E3.2 (external competing activity), D1.2 (overlooked side effects) and 

E3.3 (internal competing activity). It is interesting to underline that concerning FR the 

C1 and D1 related specific causes and E3 and E6 related specific causes are the 

most used. This means that for this subgroup there are attention-distraction and 

situation comprehension driver related problems. 

Figure 49 and in Figure 50 report the FR relation charts between critical events and 

first level causes and between the causes respectively. 


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Figure 49, FR relation chart between critical events and first level causes 


Page 51


Figure 50, FR relation chart between causes 


Page 52


Figure 49 shows that A1 has strong links with B1 (45 links) and C1 (29). In addition 

A4, the 2nd most used critical event, has very important links with C1 (30 links) and 

B1 (24 links). A5, the 3rd most used critical event has a relevant link with B1 (13 

links). The other links do not seem to be relevant. 

Figure 50 shows that B1 is the most used cause and it has strong links with E3 (42 

links), C1 (16 links), E6 (12 links) and E4 (Fatigue, 8 links). The 2nd most used 

cause is C1 that has strong links with J2 (14 links) and J1 (9 links). Finally D1 is the 

3rd most used causes and shows a relevant link with L2 (insufficient knowledge, 8 

links). 

The context variables analyses are reported from to Figure 51 to Figure 58 

149 vehicles 

149 vehicles 

Figure 51, Accidents by day of the week 

Figure 52, Accidents by month of the year 

149 vehicles 

Figure 53, Accidents by time of the day 

Figure 55, Drivers gender 

Figure 54, Drivers age 

Figure 56, Context of the accidents 


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149 vehicles


Figure 57, Traffic flows 

149 vehicles 


average pre-impact speeds 

149 vehicles 

Concerning when the accidents occurs it is possible to say that most of the accidents 

collected happened on Thursday (21%), Wednesday (19%) and Friday (16%). Most 

of the accidents happened in November (13%) and in August (11%) and in the 

afternoon (55%) or in the morning (33%). 

Concerning drivers, a large majority are male (76%) and the driver age distribution 

show that the large majority of the drivers (46%) are in the age category 25-49 years. 

20% of drivers are in the 45-65 years category. Drivers younger than 25 years old are 

22% of the sample and only 7% of the drivers are 65 years old or older. 


areas (58%), 35% of the accidents happen in rural areas and only 7% happen in 

mixed areas. The traffic flow is normal in 50% of the accidents, heavy in 27% and 

light only in 22% of the accidents. 

About the average speed limits and the average pre-impact speeds the results are 

reported above and show that the average speed limit is high in the rural areas about 

88 kph, low in mixed areas, about 71 kph and very low in urban areas, only 55 kph. 

Concerning average pre-impact speeds they are much lower than the average speed 

limits but higher than the RF ones. Indeed rural average pre-impact speed is about 

57 kph, urban one is about 28 kph and mixed is about 14 kph. 

Being struck by a vehicle which has left its lane (S) 

In the S sub group there are 217 vehicles for a total of 294 chains. The distribution of 

critical events is reported in Figure 59, the distribution of the first level causes is 


61 and the specific cause distribution is reported in Figure 62. 


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Figure 59, Critical event in S subgroup 



213 critical events 258 critical events 

Figure 60, First level causes in S subgroup 



294 specific causes 

288 last general causes 

Figure 61, Last general causes in S 



Figure 62, Specific causes in S subgroup 



Figure 59 shows that A1 (timing) is the most used critical event (72%) and the other 

critical events used are A5 (speed) and A6 (direction) both used in the 8% of the 

SNACS. The most used first general cause (see Figure 60) is C1 (55%) followed by 

B1 (20%) and D1 (17%). The most used last general causes (see Figure 61) are J2 

(30%), C1 (18%), D1 (12%) and J1 (7%). the most used specific causes are reported 

in Figure 62 and, without considering J2.4 and J1.4, the most used specific causes 

are D1 or C1 related followed by B1.4, H5.1 (permanent sight obstruction due to the 

vehicle design) and E3.2 (distraction, external competing activity).These results 

underline that there is probably problems related to the driver comprehension of the 

situation also for S subgroup vehicles. 

The S relation charts between critical events and first level causes and between the 

causes are reported in Figure 63 and in Figure 64. 


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Figure 63, S relation chart between critical events and first level causes 


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Figure 64, S relation chart between causes 


Page 57


Looking at Figure 62 A1 is the most used critical event (155 times) and has a very 

strong link with C1 (118) and relevant links with B1 (52) and D1 (12 links). Another 

two critical events A5 and A6 have been used (17 times each) and have good links to 

D1 (8-10 links). The other critical events are not relevant. 

Looking at Figure 63, C1 is the most used general cause (158 times) and has very 

strong links with J2 (89 links) and J1 (15 links). This is due to the use of the standard 

chain. Also B1 and D1 are quite used often as a general cause (both 52 times both). 

B1 has relevant links with E3 (10 links), E6 (8 links) and H5 (9 links). D1 has a 

relevant link only with H5 (8 links). 

The context variable analyses are reported from Figure 65 to Figure 72. 

217 vehicles 

Figure 65, Accident by day of the week 

217 vehicles 

Figure 66, Accident by month of the year 

Figure 67, Accident by time of the day 


Figure 68, Driver Gender 

Figure 69, Driver age 


Figure 70, Context of the accident 


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217 vehicles 

Figure 71, Traffic flow at the accident 

moment 


average pre-impact speeds distribution 

Concerning when the accidents occurs, it is possible to say that most of the 

accidents collected happen on Wednesday (20%), Friday (19%) and Tuesday (17%). 

Most of the accidents happen in November (13%) and in August (11%) and in the 

afternoon (42%) or in the morning (42%). 

Concerning drivers, a large majority are male (71%) and the driver age distribution 

shows that the large majority of the drivers (47%) are in the age category 25-49 

years. 31% of the drivers are in the 45-65 years category. Drivers younger than 25 

years old are 9% of the sample and 7% of the drivers are 65 years old or older. 


areas (49%), 40% of the accidents happen in rural areas (more than in the other 

subgroups) and 11% happen in mixed areas. The traffic flow is normal in 53% of the 

accidents, heavy in 13% and light in 30% of the accidents. 

About the average speed limits and the average pre-impact speeds, the results are 

reported above and show that the average speed limit is higher in the rural areas - 

about 101 Kph, lower in mixed areas,- about 70 Kph and much lower in urban areas - 

only 56 kph. Concerning average pre-impact speeds, they are much lower than the 

average speed limits but higher than the RF ones. Indeed rural average pre-impact 

speed is about 53 Kph, urban is about 28 kph and mixed is about 38 kph. 


Page 59



In the Being struck from behind (RF) subgroup there are 161 vehicles and a 

total of 209 SNACS chains. The results show that the standard chain has 

been used often in this subgroup. This is normal because this subgroup 

includes vehicles that were struck from behind and in this case the main driver 

problem is that s/he did not understand what was going on because of a 

missing communication with the other drivers (J1) or with the road 

environment (J2). 

The most used specific causes, excluding J1.4 and J2.4, are C1.1 (Error in 

mental model) and D1.2 (overlooked side effect). Both of them are related, as 

is the standard chain, to a missing comprehension of the situation. 

Results show a very strong relationship between A1(‘Timing’) and C1 (‘Faulty 

diagnosis’) (60 links), A3 (‘Force’) and C1 (‘Faulty diagnosis’)(13 links) and A3 

(‘Force’) and D1 (‘Inadequate plan’) (11 links). The A1 (‘Timing’)- C1 (‘Faulty 

diagnosis’) strong link is related to the high number of standard chains – the 

same explanation can probably be applied to A3 (‘Force’)- C1 (‘Faulty 

diagnosis’). The high number of A3 (‘Force’) - D1 (‘Inadequate plan’) links is 

probably due to inadequate planning of the manoeuvre. 

The links among causes show, as expected, a very strong relation between 

C1 and J1 and C1 and J2. No other relevant links are observed in the chart. 

Concerning drivers’ gender, a large majority are male (65%) and the driver 

age distribution show that the large majority of the drivers (49%) are in the 

age category 25-49 years. 30% of the drivers are in the 45-65 years category. 

Drivers younger than 25 years old are 12% of the sample and only 7% of the 

drivers are 65 years old or older. 

Regarding the accident local area, the majority of the accidents happen in 

urban areas (59%), 33% of the accidents happen in rural areas and only 8% 

happen in a mixed area. The traffic flow is normal in 50% of the accidents, 

heavy in 32% and light for only 17% of the accidents. 

Regarding the average speed limits and the average pre-impact speeds the 

results show that the average speed limit is high in the rural areas - about 90 

kph, low in mixed areas, about 80 kph, and very low in urban areas - only 54 

kph. Concerning average pre-impact speeds, they are much lower than the 

average speed limit ones. Indeed in rural areas the average pre-impact speed 

is about 22 kph, urban is about 12 kph and mixed is about 6 kph. 

In the Striking vehicle in front (FR) subgroup there are 149 vehicles for a total 

of 244 chains. A1 (timing) is the most used critical event followed by A4 

(distance) and A5 (speed). The most used first level cause is B1 (observation 

missed), followed by C1 (faulty diagnosis) and D1 (inadequate plan). C1, E3 

(Distraction), D1 and E6 (inattention) are the most used last general causes. 


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Concerning the specific causes, the most used is C1.1 (error in mental 

model), followed by E3.2 (external competing activity), D1.2 (overlooked side 

effects) and E3.3 (internal competing activity). It is interesting to underline that 

concerning FR the C1 and D1 related specific causes and E3 and E6 related 

specific causes are the most used. This means that for this subgroup there 

are attention-distraction and situation comprehension driver related problems. 

The FR relation charts show that A1 has strong links with B1 (45 links) and C1 

(29 links). In addition A4, the 2nd most used critical event, has very important 

links with C1 (30 links) and B1 (24 links). A5, the 3rd most used critical event 

has a relevant link with B1 (13 links). The other links don’t seem to be 

relevant. 

Concerning the causes links B1 is the most used cause and it has strong links 

with E3 (42 links), C1 (16 links), E6 (12 links) and E4 (fatigue, 8 links). The 

2nd most used cause is C1 that has strong links with J2 (14 links) and J1 (9 

links). Finally D1 is the 3rd most used cause and shows a relevant link with L2 

(insufficient knowledge, 8 links). 

Concerning drivers, a large majority are male (76%) and the driver age 

distribution show that the large majority of the drivers (46%) are in the age 

category 25-49 years. 20% of drivers are in the 45-65 years category. Drivers 

younger than 25 years old are 22% of the sample and only 7% of the drivers 

are 65 years old or older. 


urban areas (58%), 35% of the accidents happen in rural areas and only 7% 

happen in mixed areas. The traffic flow is normal in 50% of the accidents, 

heavy in 27% and light only in 22% of the accidents. 

About the average speed limits and the average pre-impact speeds the results 

show that the average speed limit is high in the rural areas about 88 kph, low 

in mixed areas, about 71 kph and very low in urban areas, only 55 kph. 

Concerning average pre-impact speeds they are much lower than the average 

speed limits but higher than the RF ones. Indeed rural average pre-impact 

speed is about 57 kph, urban one is about 28 kph and mixed is about 14 kph. 

In the Being struck by a vehicle which has left its lane (S) subgroup there are 

217 vehicles for a total of 294 chains. Results show that A1 (timing) is the 

most used critical event (72%) and the other critical events used are A5 

(speed) and A6 (direction) both used in the 8% of the SNACS. The most used 

first general cause is C1 (55%) followed by B1 (20%) and D1 (17%). The most 

used last general causes (see Figure 61) are J2 (30%), C1 (18%), D1 (12%) 

and J1 (7%). The most used specific causes are reported in Figure 62 and, 

without considering J2.4 and J1.4, the most used specific causes are D1 or 

C1 related followed by B1.4, H5.1 (permanent sight obstruction due to the 

vehicle design) and E3.2 (distraction, external competing activity). These 

results underline that there is probably problems related to the driver 

comprehension of the situation also for S subgroup vehicles. 


Page 61


A1 is the most used critical event (155 times) and has a very strong link with 

C1 (118) and relevant links with B1 (52) and D1 (12 links). Another two critical 

events A5 and A6 have been used (17 times each) and have good links to D1 

(8-10 links). The other critical events are not relevant. 

C1 is the most used general cause (158 times) and has very strong links with 

J2 (89 links) and J1 (15 links). This is due to the use of the standard chain. 

Also B1 and D1 are quite often used as a general cause (both 52 times). B1 

has relevant links with E3 (10 links), E6 (8 links) and H5 (9 links). D1 has a 

relevant link only with H5 (8 links). 

Concerning drivers, a large majority are male (71%) and the driver age 

distribution shows that the large majority of the drivers (47%) are in the age 

category 25-49 years. 31% of the drivers are in the 45-65 years category. 

Drivers younger than 25 years old are 9% of the sample and 7% of the drivers 

are 65 years old or older. 


urban areas (49%), 40% of the accidents happen in rural areas (more than in 

the other subgroups) and 11% happen in mixed areas. The traffic flow is 

normal in 53% of the accidents, heavy in 13% and light in 30% of the 

accidents. 

About the average speed limits and the average pre-impact speeds, the 

results show that the average speed limit is higher in the rural areas - about 

101 Kph, lower in mixed areas,- about 70 Kph and much lower in urban areas 

- only 56 kph. Concerning average pre-impact speeds, they are much lower 

than the average speed limits but higher than the RF ones. Indeed rural 

average pre-impact speed is about 53 Kph, urban is about 28 kph and mixed 

is about 38 kph. 

An overview on the three analyzed subgroups shows differences and 

similarities that can be summarized as follows: 

In the RF and S subgroups there is a frequent use of the standard chains. 

This could be related also to a difficultly of the SNACS to analyse situations in 

which the driver involved in the accident is passive (no action). In contrast in 

the FR subgroup the standard chain is not often used. 

B1 is often used as first general cause and other related causes, such as H5, 

are not used often. Observation missed seems mostly to be related to 

distraction/inattention. 

D1 and C1, mainly in FR subgroup but also in the other two subgroups, are 

the most used last general causes (without considering standard chains) and 

this could be related to a problem of situation comprehension. 

E3 and E6, in FR subgroup, are used often as last general causes. Also in 

subgroup S E3 and E6 are often used. 


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Considering also the context variables, some other conclusions could be 

drawn: 

In all the subgroups the majority of the drivers are male - from 65% of the 

driver male in RF subgroup to 75% in FR subgroup. 

The majority of the accidents happen in an urban area but there are some 

differences among the subgroups. RF and FR accidents in urban area are 

nearly 60% instead the S subgroup ones are about 50%. On the other hand 

the RF pre-impact speeds are less than 50% of the FR and S ones. 

Looking at these results, it seems that there are some similarities among S 

and FR subgroup vehicles and a hypothesis can be drawn for FR subgroup 

accidents and also for S ones. The large use of C1, D1, E3 and E6 as the last 

general causes show that there are problems related to the driver’s 

comprehension of the situation or attention in these two subgroups. This 

hypothesis needs a bigger sample of accidents to be verified. 

3.3 Vehicle encountering another vehicle on crossing 

paths 

When superimposing the SNACS charts in the selected group, common 

causation patterns may be identified among several charts. However, when 

large numbers of charts are selected for aggregation the details may not be so 

evident. In this study the aggregation was done without considering the levels 

of confidence for each causal chain. This means that in the final aggregation, 

a low confidence causal chain is attributed the same importance as a causal 

links with a high level of confidence. 

A crossing paths accident is defined as a traffic conflict where one moving 

vehicle cuts across the path of another, when they were initially approaching 

from either lateral or opposite directions in such a way that they collided at or 

near a junction (Najm et al, 2001). The typical outcome is an intersection 

crash, but crashes where vehicles are backing out of driveways or making U- 

turns are also included. 

3 4 

2 5 

1 6 

8 7 

Figure 73, A junction with entry (1, 3, 5 and 7) and exit (2, 4, 6 and 8) zones and 

intersection (grey). 


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The majority of crossing path accidents occur in junctions. A junction (Figure 

73) can be seen as consisting of a number of entry zones (a lane from which 

a vehicle enters the intersection) and a number of exit zones (a lane to which 

the vehicle exits the intersection). The path of the vehicle through the junction 

can be described using these zones. 

Four conflict scenarios can be identified based on the paths of the vehicles 

through the junction (Figure 85). The first three are common in junctions. 

1. Straight Crossing Paths (SCP), see Figure 74, scenario 1 

The paths of two vehicles cross at or near a right angle. A typical SCP would 

have vehicle 1 entering in zone 1 and intending to exit in zone 6 and vehicle 2 

entering in zone 7 and intending to exit in zone 4. 

2. Left Turn Across Path-Opposite Direction (LTAP-OD), see Figure 74, 

scenario 2 

A typical LTAP-OD would have vehicle 1 entering in zone 7 and intending to 

exit in zone 2 and vehicle 2 entering in zone 3 and intending to exit in zone 8. 

3. Left Turn Across Path-Lateral Direction (LTAP-LD), see Figure 74, 

scenario 3 

A typical LTAP-LD would have vehicle 1 entering in zone 7 and intending to 

exit in zone 2 and vehicle 2 entering in zone 1 and intending to exit in zone 6. 

1. SCP 2. LTAP-OD 3. LTAP-LD 4a. LTIP 4b. RTIP 

Figure 74, Conflict scenarios SCP, LTAP-OD and LTAP-LD (left to right) as well as 

merge conflict scenarios LTIP (left) and RTIP (right). 

4. Merge conflicts, see Figure 74, scenario 4a and 4b: 

Another type of accident involving crossing paths is merge conflicts, of which 

there are two, Left Turn Into Path (LTIP) and Right Turn Into Path (RTIP), 

where one vehicle makes a left or right turn into the path of another vehicle, 

with both vehicles ending up travelling in the same direction. These occur 

when the paths of two vehicles cross each other and then merge into one 

path. Note that this is not the result of a lane change situation, those are 

covered in the “vehicle leaving its lane” trajectory group, but rather a conflict 

that appears e.g. on highway on-ramps or when lanes merge together. 

Crossing path accidents that include SVRU are not included in the analysis 


Page 64


3.3.1 Sorting 

Data for the analysis of crossing path accidents were extracted from the 

SafetyNet Accident Causation database according to the following selection 

criteria: 

• an accident had to have a GDV code (See Reed and Morris, 2008) for 

the description of the GDV-codes) that described a crossing path 

scenario (Table 14). All codes can be seen in Appendix B. 

• an accident had to have at least one SNACS analysis 

• an accident had to include no SVRU 

Each accident was then assigned to each of the following subgroups based 

on conflict scenario according to its GDV code (Table 15): 

• Straight Crossing Paths (SCP) 

• Left Turn Across Path-Opposite Direction (LTAP-OD) 

• Left Turn Across Path-Lateral Direction (LTAP-LD) 

• Merge conflicts, Left Turn Into Path (LTIP) and Right Turn Into Path 

(RTIP) 

• Other, accidents not fitting any of the above conflict scenarios 

The extracted data were then analysed in Microsoft Excel. The analysis of 

crossing path accidents was done both on all accidents as a group and on 

each of the four conflict scenarios. The group ‘Other’ was not analysed. 

Table 14, GDV codes for accidents with crossing paths (see Appendix B) 

GDV Type 2 Turning off GDV Type 3 Turning in/ GDV codes from 

accidents 

crossing accidents other accident 

types 

202, 203, 204 301, 302, 303, 304, 309 543 

211, 212, 213, 214, 215, 311, 312, 313, 314, 319 561, 562, 569 

219 

223, 224, 225 321, 322, 323, 324, 326, 571, 572, 579 

329 

232, 239 331, 332, 333, 334 714, 715 

243, 244, 245 351, 352, 353, 354, 355, 721, 722, 723, 724, 

359 

729 

261, 262, 269 361, 362, 363, 364, 369 

271 

281, 283, 285, 286 


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Table 15, GDV codes for the four different conflict scenarios (see Appendix B) 

SCP LTAP-OD LTAP-LD Merge 

conflicts 

202, 203, 204 211, 212, 215 261 213, 214, 

223, 224, 225, 232 281 302, 312, 303, 304 

326 

243, 244, 245, 271 351, 354 721 313, 314 

283, 285, 286 543 322, 332, 352 

301, 311, 321, 324, 331, 334 722, 723 

353, 355, 361, 362, 363, 

364, 369 

561, 562, 569, 572, 579 

714, 715 


The selected crossing path accidents were analysed in three ways: 

All crossing path accidents 

The selected crossing path accidents were analysed by counting the number 

of critical events, the number of first level causes, the number of links between 

critical event and first level causes and by aggregating the SNACS analyses 

to make causal factor charts. This was done both on all crossing path 

accidents and for each of the four conflict scenarios. The results were 

presented as five causal factor charts, one for all crossing path accidents and 

one for each of the four conflict scenarios SCP, LTAP-OD, LTAP-LD and 

merge conflicts. The causal factor charts contained all the aggregated SNACS 

analyses. 

Context variables 

For all crossing path accidents, the number of links from critical event to first 

level cause was counted for the context variables: age of driver (>25, 25-44, 

45-64 and 65+ years), time of day (0:00 to 5:59, 6:00 to 11:59, 12:00 to 17:59 

and 18:00 to 23:59) and speed limit (


3.3.3 Results 

All crossing path accidents 

In total, 263 crossing paths accidents were found that fulfilled the selection 

criteria. Thirteen of these were originally assigned to other groups but had 

their GDV codes changed as these accidents were crossing path accidents. 

There were 258 two-vehicle crashes and 5 three-vehicle crashes, with a total 

of 531 vehicles and 528 SNACS analyses; three cases lacked SNACS 

analysis for one of the road users and these vehicles were excluded from the 

analysis. 

The most common crashes were car to car crashes (112 accidents) and car to 

motorcycle (82 accidents). The most common conflict scenario was Straight 

Crossing Paths (Table 8). The GDV codes present among the selected 

crossing path accidents are shown in Table 16 

Table 16, Number of accidents for each conflict scenario 

Conflict scenario 

Number of accidents 

SCP 123 

LTAP-OD 59 

LTAP-LD 38 

Merge conflicts 25 

Other 18 

Total 263 

Table 17, GDV codes present in the selected crossing path accidents (bold) 

GDV Type 2 Turning off 


GDV Type 3 Turning in/ 

crossing accidents 

Other GDV codes 

used 

202, 203, 204 301, 302, 303, 304, 309 543 

211, 212, 213, 214, 215, 311, 312, 313, 314, 319 561, 562, 569 

219 

223, 224, 225 321, 322, 323, 324, 326, 571, 572, 579 

329 

232, 239 331, 332, 333, 334 714, 715 

243, 244, 245 351, 352, 353, 354, 355, 

359 

721, 722, 723, 724, 

729 

261, 262, 269 361, 362, 363, 364, 369 

271 

281, 283, 285, 286 

The number of general critical events for all crossing path accidents is shown 

in Table 18, the number of first general causes is shown in Table 19, the 10 

most common links are shown in Table 20 


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Table 18, Number of general critical event for all crossing path accidents. 

Number of general critical events 

A1 Timing 348 

A2 Duration 25 

A3 Force 4 

A4 Distance 42 

A5 Speed 68 

A6 Direction 15 

A7 Object 1 

A8 Sequence 25 

The general critical event A1 Timing has three specific critical events: A1.1 

Premature action, A1.2 Late action and A1.3 No action. Specific critical event 

A1.1 occurred 152 times, A1.2 78 times and A1.3 118 times. 

Table 19, Number of first level causes for all crossing path accidents. 

First level cause 

Number 

B1 Observation missed 247 

B2 False observation 8 

C1 Faulty diagnosis 204 

C2 Wrong reasoning 3 

C3 Decision error 15 

D1 Inadequate plan 126 

D2 Priority errors 2 

E5 Performance variability 1 

E6 Inattention 10 

H3 Access problems 1 

I1 Equipment failure 4 

J1 Communication failure 9 

J2 Communication failure 29 

Table 20, Ten most common links, critical event to first level causes, for all crossing 

path accidents. 

Link from critical event to first level cause Number of links 

A1 Timing - B1 Observation missed 182 

A1 Timing - C1 Faulty diagnosis 153 

A1 Timing - D1 Inadequate plan 49 

A5 Speed - D1 Inadequate plan 33 

A5 Speed - B1 Observation missed 1 23 

A5 Speed - C1 Faulty diagnosis 20 

A4 Distance - B1 Observation missed 19 

A2 Duration - B1 Observation missed 17 

A1 Timing - J2 Communication failure 14 

A4 Distance - C1 Faulty diagnosis 13 

Figure 75 to Figure 84 show the critical event charts as well as the causal 

factor charts for all crossing path accidents and the four conflict scenarios. 

The 10 most common causal factor chains from the figures are explained in 

Table 21. 


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Figure 75, Critical event chart for all crossing path accidents 


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Figure 76, Causal factor chart for all crossing path accidents (For explanation of cause codes, see Appedix A) 


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Figure 77, Critical event chart for SCP accidents 


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Figure 78, Causal factor chart for SCP accidents (For explanation of cause codes, see Appedix A) 


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Figure 79, Critical event chart for LTAP-OD accidents 


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Figure 80, Causal factor chart for LTAP-OD accidents (For explanation of cause codes, see Appedix A) 


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Figure 81, Critical event chart for LTAP-LD accidents 


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Figure 82, Causal factor chart for LTAP-LD accidents (For explanation of cause codes, see Appedix A) 


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Figure 83, Critical event chart for Merge conflicts 


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Figure 84, Causal factor chart for Merge conflicts (For explanation of cause codes, see Appedix A) 


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Table 21, Ten most common causal factor links for all crossing path accidents. 

Ten most Explanation 

common causal 

links 

A1-B1-N4-N4.2 Timing critical event due to a missed observation 

because of a temporary obstruction of view by 

another vehicle 


because of a permanent obstruction of view by 

vegetation 


because of a permanent obstruction of view by 

fence or building 


because of a permanent obstruction of view by other 

objects 

A1-C1-J2-J2.4 Timing critical event due to a faulty diagnosis of 

situation arising from an information failure between 

driver and environment 

A1-C1-C1.1 Timing critical event due to a faulty diagnosis of 

situation because of error in mental model 

A1-B1-C1-C1.1 Timing critical event due to a missed observation 

because of a faulty diagnosis of the situation caused 

by an error in driver’s mental model 

A1-D1-D1.2 Timing critical event due to an inadequate plan 

because the driver has overlooked the side effects 

of his/her actions 

A1-D1-D1.1 Timing critical event due to an inadequate plan 

because of error in driver’s mental model 

A1-D1-L2-L21 Timing critical event due to an inadequate plan 

because the driver has insufficient experience 


Table 22 and Table 23, show the number of drivers and accidents sorted 

according to the three context variables age of driver, time of day and speed 

limit. 


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Table 22, Number of drivers and accidents sorted according to context variables age, 

time of day and speed limit 

Age Number of drivers Percentage 


Table 23, Most common links when all crossing path accidents are sorted according to 

context variables age, time of day and speed limit. (number of links in parenthesis) 

Age Most common link Second most common link 


Left turn across path conflict scenarios 

Figure 85 and Figure 86 show the number of critical events to first level cause 

links for left-turning and straight-going vehicles for the two conflict scenarios 

LTAP-OD and LTAP-LD respectively (critical events and first level causes 

abbreviated, see Table 10 and Table 11 for explanation). The group ‘Other’ 

includes all links that only occur once. 

Figure 85, Number of critical event to first level causes for left-turning (top) and 

straight-going (bottom) for the conflict scenario LTAP-OD (For explanation of cause 



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Figure 86, Number of critical event to first level cause for left-turning (top) and straightgoing 

(bottom) for the conflict scenario LTAP-LD (For explanation of cause codes, see 

Appedix A) 

3.3.4 Discussion 

For crossing path accidents the most common critical event is timing, i.e. a 

driver takes premature, late or no action, followed by critical events related to 

speed and distance. The timing critical event occurs more often than all other 

critical events put together. This is not surprising as crossing path accidents 

occur because at least one vehicle is in the wrong place at the wrong time. As 

can be seen in Table 13, most crossing path accidents stem from missed 

observations (i.e. an external cause) due to vegetation, other vehicles or 

buildings or driver behaviour such as faulty diagnosis and inadequate plans 

(i.e. an internal cause). The causes stemming from driver behaviour often 

have the specific causes C1.1/D1.1 Error in mental model or D1.2 Overlooked 

side effects. This indicates that a lack of situation awareness contributs to the 

accident. 


Page 83


It is also interesting to note that very few crossing paths accidents are caused 

by vehicle malfunctions, further indicating that the study of driver behaviour is 

an important part of reducing crossing path accidents. 


For the context variables age of driver, time of day and speed limit, the 

differences in critical events and first level causes were not great. 

For the context variable age of driver, drivers are fairly evenly distributed 

across the three lower age groups, (-25, 25-44 and 45-64 years with 20, 34 

and 24% of drivers respectively) with the greatest number between 25 and 44 

years old. This corresponds to the age of those most active in traffic. For all 

age groups, the most common link from critical event to first level cause is A1 

Timing to B1 Observation missed followed by A1 Timing to C1 Faulty 

diagnosis. 

For the context variable time of day, most accidents (421 accidents) occur 

during the day, i.e. 06:00 to 17:59, and the majority of these (237 accidents) 

occur between 12:00 and 17:59. Neither of these results are surprising, since 

more vehicles are on the roads during the day, the risk of an accident is 

higher. Also, the two most common links are A1 Timing to either B1 

Observation missed or C1 Faulty diagnosis, except for the time span 00:00 to 

05:59, were the two most common links are A1 Timing to either B1 

Observation missed or D1 Inadequate plan. The number of accidents in the 

time span 00:00 to 05:59 is however low (14 accidents) so a generalisation 

from this is difficult. 

For the context variable speed limit, most accidents occur when the speed 

limit is 50 km/h or less, with few accidents occurring when the speed limit is 

90 km/h or greater. This indicates that most crossing path accidents occur in 

urban areas where there are more opportunities for crossing path accidents 

due to road layout. For all speed limits, the most common links from critical 

event to first level cause is A1 Timing to B1 Observation missed, followed by 

A1 Timing to C1 Faulty diagnosis, except when the speed limit is above 90 

km/h. Then the most common links from critical event to first level cause is A1 

Timing to C1 Faulty diagnosis, followed by A1 Timing to B1 Observation 

missed. A possible explanation for this is that where the speed limit is 90 km/h 

or above, there are fewer problems with visibility and more accidents occur 

due to driver-related issues. The small difference in the number of links and 

the low number of accidents (29 accidents) makes generalisation difficult. 

For all context variables, the most common link from critical event to first level 

cause was A1 Timing to B1 Observation missed. However, these context 

variables should be further studied by constructing causation link charts for 

them. A conclusion that can be drawn from this analysis is that there are no 

major differences in causation for crossing path accidents due to age of driver, 

time of day and speed limit. 


Page 84


Left turn across path conflict scenarios 

For left turn across path conflict scenarios, there is a difference in causation 

for left-turning and straight-going vehicles. 

In LTAP-OD scenarios, the most common causation links for left-turning 

vehicles are A1-B1-N2/N4 (i.e. a timing critical event caused by a missed 

observation due to a permanent or temporary obstruction of view) and A1-C1- 

C1.4 (i.e. timing critical event caused by a faulty diagnosis of the situation 

stemming from a misjudgement of time/distance, indicating a situation 

awareness problem). If the specific critical event is taken into consideration, 

the most common links are A1-A1.1-B1-N2/N4 and A1-A1.1-C1-C1.4, where 

A1.1 is a timing critical event where an action, in this case a left turn, is 

initiated too early. 

For straight-going vehicles, i.e. the vehicle going straight through a junction, 

the most common causal links are A1-C1-J1 and A1-C1-J2, i.e. a faulty 

diagnosis of the situation due to communication failures between drivers or 

between driver and environment. If the specific critical event is taken into 

consideration, the most common causal link is A1-A1.2-C1-J1/J2, i.e. a late 

action caused by a faulty diagnosis of the situation due to communication 

failures either between drivers or between this driver and the environment. A 

possible explanation for this is that the left-turning vehicle does not indicate 

that it is going to turn. 

When the causal links for the two vehicles are combined, the main causes for 

an LTAP-OD accident can be seen: the driver of the left-turning vehicle either 

does not see the other vehicle or misjudges its speed and the straight-going 

vehicle does not realise that the other vehicle is going to turn. 

In LTAP-LD scenarios, much is similar to LTAP-OD scenarios. The most 

common causation links for left-turning vehicles are the same: A1-B1-N2/N4 

(a timing critical event caused by a missed observation due to a permanent or 

temporary obstruction of view) and A1-C1-C1.4 (a timing critical event caused 

by a faulty diagnosis of the situation stemming from a misjudgement of 

time/distance). If the specific critical event is taken into consideration, the 

most common links are A1-A1.1-B1-N2/N4 and A1-A1.1-C1-C1.4, where A1.1 

is a timing critical event where an action, in this case a left turn, is initiated too 

early. This indicates that left-turning drivers face similar problems of 

observation and situation awareness regardless of where the other vehicle is 

coming from. 


the most common causal links are A1-C1-J2 (a timing critical event caused by 

a faulty diagnosis of the situation due to communication failures between 

drivers or between driver and environment), and A5-D1-D1.2 (a speed-related 

critical event caused by an inadequate plan due to overlooked side effects). If 

the specific critical event is included in these causal links, the most common 

causal link is A1-A1.2-C1-J1/J2, i.e. a late action caused by a faulty diagnosis 

of the situation due to communication failures either between drivers or 

between this driver and the environment. The second most common causal 


Page 85


link then becomes A5-A5.1-D1-D1.2 (a critical event where the speed of the 

vehicle is too high, caused by an inadequate plan due to overlooked side 

effects). A possible explanation for both these causal links is that the leftturning 

vehicle does not indicate that it is going to turn. 

When the causal links for the two vehicles are combined, the main causes for 

an LTAP-LD accident can be seen: the driver of the left-turning vehicle either 

does not see the other vehicle or misjudges its speed and the driver of the 

straight-going vehicle either does not realise that the other vehicle is going to 

turn or drives too fast without realising the effects this has on other drivers. 

The conflict scenario analysis was originally intended to include the conflict 

scenario SCP, sorted according to vehicle with and without priority (i.e. right of 

way) but there is no easy way to assign priority to each vehicle in a crash 

because this is not a specific variable and time constraints precluded a deeper 

study. In future databases, the variable “priority” should be assigned to each 

vehicle with values yes or no. However, based on the results from the left turn 

across path conflict scenario analysis, similar result for the SCP scenario 

could be expected. 

3.3.5 Conclusions 

• For all crossing path accidents, the most common critical event is 

timing, i.e. a driver takes premature, late or no action. The most 

common first level causes for this are missed observations or faulty 

diagnosis of the situation. 

• There are no major differences in causation for crossing path accidents 

due to age of driver, time of day and speed limit. Timing is the most 

common critical event with observation missed and faulty diagnosis 

being the most common first level causes. 

• For all four crossing path conflict scenarios (SCP, LTAP-OD, LTAP-LD 

and merge conflicts), the most common critical event is timing. The 

most common general causes are missed observations, faulty 

diagnosis and inadequate plans. 

• For future accident databases, a variable that assigns priority or not to 

each vehicle should be introduced. 

3.4 Accidents involving vulnerable road users 

When superimposing the SNACS charts in the selected group, common 

causation patterns may be identified among several charts. However, when a 

large number of charts are selected for aggregation the details may not be so 

evident. In this study the aggregation was done without considering the levels 

of confidence for each causal chain. This means that in the final aggregation, 

a low confidence causal chain is attributed the same importance as a causal 



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3.4.1 Sorting 

Accidents were selected using the vehicle type variable. All accidents 

including a shoe vehicle (pedestrian) or a bicycle were included. For further 

analysis a new variable was constructed by merging case number and vehicle 

number variables. This was used to select pedestrians and bicycles and 

distinguish these from opponent vehicles. 

The analysis results are divided into several parts. First there is a general 

description of the investigated accidents where a pedestrian or bicyclist was 

involved. Then SNACS analyses are reported in tables and charts. 

Pedestrians and vehicles in pedestrian accidents are dealt with separately. 

The same procedure is applied to bicyclists and vehicles in bicycle accidents. 

In the SNACS “charts”, grey links without numbers indicate less than five links 

(in charts showing all participants) or less than three links (in rest of the 

charts). The context variables “age” and “time of day” were also analysed. The 

context variable “speed limit” was also meant to be analysed separately, 

however the majority of accidents including vulnerable road users occurred in 

speed limit area of 50 kph or less and analysis would not have given much 

added value. Finally some conclusions are drawn from the results. 


Here is a general description of accidents: 

There were a total of 180 accidents where a slowly moving vulnerable road 

user (VRU) was involved. Pedestrians were involved in 87 accidents and 

bicyclists in 93 accidents (Table 24). 

In four accidents a motor vehicle and two pedestrians were involved and in 

one accident two cars and three pedestrians were involved. Two accidents 

were collisions between two bicycles; these bicyclists are included both as 

bicycles and as opponents in the analysis. Two accidents were single vehicle 

accidents with only a bicycle. The total number of involved pedestrians was 92 

and involved bicyclists 95. 

One accident was a person with motorized wheelchair. This is included in the 

pedestrian numbers. 

Table 24, Number of VRU accidents 

Accidents Involved 

SVRUs 

Involved 

opponents 

Pedestrian 87 92 87 

Bicycle 93 95 90 

Total 180 187 177 

Most of the investigated accidents occurred during the daytime and 

weekdays; see Table 25 and Table 26. 


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Table 25, Accident day 

Monday Tuesday Wednesday Thursday Friday Saturday Sunday Total 

Pedestrian 16 17 17 11 14 10 2 87 

Bicycle 18 23 14 18 13 4 3 93 

Total 34 40 31 29 27 14 5 182 

Table 26, Accident day and time of day 

Time / Day Monday Tuesday Wednesday Thursday Friday Saturday Sunday Total 

0 – 6 1 1 2 

6 – 12 14 21 16 14 16 6 87 

12 – 18 13 15 12 11 7 5 4 67 

18 – 24 6 4 3 4 3 3 1 24 

Total 34 40 31 29 27 14 5 180 

Most of the accidents occurred in daylight. Pedestrian accidents occurred in 

darkness more often than bicycle accidents (Figure 87). 

90 

80 

70 

60 

50 

40 

Pedestrians 

Bicyclists 

30 

20 

10 

0 

Darkness 

Darkness with 

artificial light 

Daylight 

Partial light 

Figure 87, Light conditions 

Age and gender of vulnerable road users were distributed very evenly. The 

largest age group was under 25 years old, but the size of 45–64 years old and 

over 64 years old groups were almost the same (Table 27). 


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Table 27, Driver age and gender (two accidents with two bicyclists involved 

Pedestrians Bicyclists Total 

Age (years) Female Male Female Male Unknown 


Most of the accidents took place in speed limit areas of 50 kph or less (Table 

28). Known pre-impact speeds of the vehicles reflected these limits as can be 

seen in Table 29, however in many cases pre-impact speeds were unknown. 

Table 28, Speed limit of opponent vehicle 

Speed limit Bicycle Pedestrian Total 


3.4.3 Results 

In vulnerable road user accidents, “Timing” was the most common critical 

event. Within timing “Premature action” and “No action” were the prevalent 

critical events (Figure 90). 

7 % 

1 % 

7 % 

4 % 

13 % 

1 % 

4 % 

22 % 

22 % 

13 % 

0 % 6 % 




A2.1 Prolonged action / movement 








A8.1 Skipped action 

Figure 90, Specific critical events for all participants in VRU accidents 

Naturally timing was the source for most common links to 1st level causes. 

Strongest links led to “Observation missed” and “Faulty diagnosis”. 


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Figure 91, SNACS chart of links from critical event to 1st level causes. All participants 

in accidents involving vulnerable road users. 

Within causes, the strongest link was between “Faulty diagnosis” and 

“Information failure”. “Faulty diagnosis” was present as a 1st and 2nd level 

cause, while “Observation missed” was usually present only as 1st level 

cause (Figure 92) 


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Figure 92, SNACS chart of cause to cause links. All participants in accidents involving vulnerable road users. 


Page 93


Comparing pedestrians and bicyclists, results were quite similar. Timing 

represented the most frequent critical event (Table 30). 

Table 30, Most frequent SNACS links for pedestrians and bicyclists 

Vulnerable Road Users 

Pedestrians (n = 92) Bicyclists (n = 95) 

Critical A1 Timing 62 A1 Timing 43 

Events A4 Distance 11 A6 Direction 18 

A1C1 

Timing - 

Timing - Faulty 

30 A1B1 Observation 

diagnosis 

missed 

21 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 

A1B1 

A1D1 

C1J2 

B1D1 

B1E3 

B1N4 

Timing - 

Observation 

missed 

Timing - 



- Information 

failure 

Observation 

missed - 


Observation 

missed - 

Distraction 

Observation 

missed - 

Temporary 

obstruction 

view 

to 

27 A1C1 

16 A6D1 

16 C1J2 

10 B1C1 

10 B1D2 


diagnosis 

Direction - 




Observation 

missed - Faulty 

diagnosis 

Observation 

missed - Priority 

error 

Pedestrian accidents 

The most frequent critical event for pedestrians was “Timing”, which 

accounted for 68 percent of all critical events for pedestrians. Usually action 

was performed prematurely (33 cases) which tells that the pedestrian stepped 

on the roadway before the opponent vehicles had passed the site (Figure 93). 

For vehicles in pedestrian accidents the most frequent critical event was also 

“Timing”, which accounted for 59 percent of all critical events for drivers. 

However there was usually no action performed before the collision (27 

cases) (Figure 94). 

10 

20 

15 

13 

6 

6 


Page 94


3 % 

8 % 

3 % 

3 % 

9 % 

6 % 

24 % 

7 % 

37 % 










Figure 93, Specific critical events for pedestrians 

6 % 

7 % 

9 % 

10 % 

4 % 

9 % 

2 % 

1 % 

31 % 

21 % 











Figure 94, Specific critical events for vehicles in pedestrian accidents 

For pedestrians the most frequent links from “Timing” to 1st level causes were 

to “Faulty diagnosis” and “Observation missed”. (Table 31, Figure 95 and 

Figure 96) For drivers in pedestrian accidents “Observation missed” was by 

far the most frequent 1st level cause. 

For vehicle drivers in pedestrian accidents the observation was missed 

somehow in most cases. Explanations for this can be found from cause to 

cause links which indicate that there were temporary obstructions to view. 


Page 95


Table 31, Most frequent SNACS links for pedestrians and vehicles in pedestrian 


Vulnerable road users 

Pedestrians (n = 92) Vehicles in pedestrians (n = 89) 


Events A4 Distance 11 A4 Distance 12 

A1C1 

Timing - 


30 A1B1 Observation 

diagnosis 

missed 

40 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 

A1B1 

A1D1 

C1J2 

B1D1 

B1E3 

Timing - 

Observation 

missed 

Timing - 




failure 

Observation 

missed - 


Observation 

missed - 

Distraction 

27 A1C1 

16 A4B1 

16 B1N4 

10 C1J2 

10 B1E3 


diagnosis 

Distance - 

Observation 

missed 

Observation 

missed - 

Temporary 

obstruction to view 



Observation 

missed - 

Distraction 

19 

10 

17 

10 

9 


Page 96


Figure 95, SNACS links from critical events to 1st level causes: pedestrians. 


Page 97


Figure 96, SNACS links causes to causes: pedestrians 


Page 98


Figure 97, SNACS links from critical events to 1st level causes: vehicle drivers in pedestrian accidents 


Page 99


Figure 98, SNACS links from causes to t causes: vehicle drivers in pedestrian accidents 


Page 100


Bicycle accidents 

The most frequent critical event for bicyclists was timing (47 %). Usually this 

was premature action or no action (Figure 99). 

4 % 

19 % 

3 % 

2 % 

14 % 

2 % 

9 % 

25 % 

19 % 

3 % 











Figure 99, Specific critical events for bicyclists 

For opponent vehicle drivers in bicycle accidents “premature action” was the 

most frequent specific critical event followed by “prolonged distance”(Figure 

100) 

1 % 5 % 

4 % 

9 % 

20 % 

4 % 

25 % 

18 % 












1 % 1 % 

12 % 

Figure 100, Specific critical events for vehicles in bicycle accidents 

For bicyclists missed observations and faulty diagnosis were the most 

frequent causes. For vehicle drivers the situation was quite similar to that of 

drivers in pedestrian accidents: observations were missed in the first place 

(Figure 101 and Figure 102). Cause to cause links indicate that in bicycle 

accidents information failures were most frequent risk factor for opponent 

vehicle drivers as well (Table 32, Figure 101 and Figure 102). 


Page 101


Table 32, Most frequent SNACS links for bicyclists and vehicles in bicycle accidents 

Vulnerable Road Users 

Bicyclists (n = 95) Vehicles in bicycle accidents (n = 91) 


Events A6 Direction 18 A4 Distance 22 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 

A1B1 

A1C1 

A6D1 

C1J2 

B1C1 

B1D2 

Timing - 

Observation 

missed 


diagnosis 

Direction - 




failure 

Observation 

missed - Faulty 

diagnosis 

Observation 

missed - Priority 

error 

21 A1B1 

20 A1C1 

15 A4B1 

13 C1J2 

6 B1C1 

6 B1N2 

Timing - Observation 

missed 


diagnosis 

Distance - Observation 

missed 



Observation missed - 



Permanent obstruction 

to view 

36 

16 

14 

20 

12 

10 


Page 102


Figure 101, SNACS links from critical events to 1st level causes: vehicle drivers in pedestrian accidents 


Page 103


Figure 102, SNACS links from critical events to 1st level causes: vehicle drivers in bicycle accidents 


Page 104


Figure 103, SNACS links from causes to t causes: bicyclists 


Page 105


Figure 104, SNACS links from causes to causes: vehicles in bicycle accidents 


Page 106


Context analysis 

Time of day (4 blocks 00:00-05:59, 06:00-11:59, 12:00-17:59; 18:00-23:59) 

Pedestrians 

The investigated pedestrian accidents occurred during daytime. Only two 

accidents occurred during early hours of the day. For the rest of the accidents, 

“Timing” was again the most frequent critical event. In three time blocks the 

most frequent 1st level causes were “Faulty diagnosis” and “Observation 

missed” with links from “Timing”. In the cause to cause links there was more 

variation. “Information failures” during the morning and “influence of 

substances” in the evening were observed (Table 33). The frequencies of the 

cells are quite low, so conclusions should not be taken too far. 


Page 107


Table 33, Most frequent SNACS links for pedestrians according to time of day 

Pedestrians – Time of Day 

0-6 (n=2) 6-12 (n=46) 

Critical A4 Distance 1 A1 Timing 30 

Events A8 Sequence 1 A4 Distance 7 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 

A4D1 

A8D1 

D1E7 

Distance - 


Sequence - 



- Under the 

influence of 

substances 

Pedestrians - Time of Day 

1 A1C1 

1 A1B1 

A1D1 

2 C1J2 

B1D1 

B1E3 


diagnosis 

Timing - 

Observation 

missed 

Timing - 




Observation 

missed - 


Observation 

missed - 

Distraction 

16 

13 

7 

11 

6 

6 

Critical 

Events 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 

12-18 (n=30) 18-24 (n=13) 

A1 Timing 20 A1 Timing 12 

A4 Distance 7 A4 Distance 1 

A1B1 

Timing - 


Observation 10 A1C1 

diagnosis 

missed 

6 

A1C1 

A1D1 

B1N4 

B1D1 

C3E3 


diagnosis 

Timing - 


Observation 

missed - 

Temporary 

obstruction to 

view 

Observation 

missed - 


Decision error - 

Distraction 

8 A1B1 

6 A1C3 

A1D1 

7 B1E7 

4 C1J2 

4 D1E7 

Timing - 

Observation 

missed 

Timing - Decision 

error 

Timing - 


Observation 

missed- Under the 

influence of 

substances 




Under the 

influence of 

substances 

4 

3 

3 

3 

3 

2 


Page 108


Vehicles in pedestrian accidents 

For drivers in the pedestrian accidents “Timing” was most frequent critical 

event in all four time blocks. Observations were missed throughout the day. 

Causes for missing the observation scattered into different links, the most 

frequent being obstruction to view (Table 34). 

Table 34, Most frequent SNACS links for vehicles in pedestrian accidents according to 

time of day 

Vehicles in pedestrians – Time of Day 

0-6 (n=2) 6-12 (n=45) 


Events A4 Distance 1 A4 Distance 11 

Timing - 

A1B1 

Critical 

Observation missed 1 A1B1 Timing - Observation 

missed 

17 

Event to Distance - 

A4B1 

1st level Observation missed 1 A1C1 Timing - Faulty 

diagnosis 

9 

cause links Distance - 


A4D1 

1 A4B1 


missed 

6 

Cause to 

Cause links 

B1N4 


- Temporary 


2 C1J2 

B1E3 

B1N4 

Vehicles in pedestrians – Time of Day 




Distraction 


Temporary obstruction 

to view 

6 

5 

5 

Critical 

Events 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 

12-18 (n=30) 18-24 (n=13) 

2 

A1 Timing 

A1 Timing 8 

0 

A5 and 

A4 Distance 4 Speed and Direction 2 

A6 

Timing - 1 

A1B1 


7 

missed 



A1C1 

8 A1C1 

2 

diagnosis 

diagnosis 

Distance – 

A4B1 

3 multiple multiple 1 

B1N4 

B1E6 

B1C1 

C1J2 



- Temporary 



- Inattention 


- Faulty diagnosis 



8 B1E3 


Distraction 

4 multiple 2 

3 

3 

3 


Page 109


Bicycles 

No bicycle accidents were investigated in the early hours of the day. Within 

other time blocks there was not much deviation in critical events or in links to 

1st level causes. “Timing” remained the most frequent critical event and 

missed observations and faulty diagnoses the most frequent 1st level causes. 

In cause to cause links frequencies are small, but information failures are the 

most frequent (Table 35). 

Table 35, Most frequent SNACS links for bicyclists according to time of day 

Bicycles – Time of Day 

0-6 (n=0) 6-12 (n=47) 

Critical 

A1 Timing 17 

Events 


Critical 

A4C1 Distance - Faulty diagnosis 9 

Event to 


A1B1 

1st level 

missed 

8 

cause links 

A1C1 Timing - Faulty diagnosis 8 

C1J2 


7 

Cause to 

Cause links 

Bicycles – Time of Day 

B1N4 

B1C1 



Temporary obstruction to 

view 

Observation missed - Faulty 

diagnosis 

4 

3 

Critical 

Events 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 

12-18 (n=35) 18-24 (n=11) 


A4 Distance 13 A5 A6 Speed and Direction 1 

A1B1 

Timing - 

Observation missed 8 A1B1 Timing- Observation 

missed 

5 

A1C1 



8 A1C1 

diagnosis 

diagnosis 

4 

A6D1 

Direction - 

Timing - Inadequate 

6 A1D1 


plan 

2 

C1J2 



6 B1D1 

2 



B1D1 


- Inadequate plan 

3 multiple links 1 


B1N2 - Permanent 3 


Vehicles in bicycle accidents 

During the morning hours, “Distance” was the most frequent critical event and 

“Timing” in other times of the day. Missed observations were the most 

frequent 1st level causes throughout the day (Table 36). 


Page 110


Table 36, Most frequent SNACS links for vehicles in bicycle accidents according to 

time of day 

Vehicles in bicycles – Time of Day 

0-6 (n=0) 6-12 (n=47) 

Critical 


Events 

A1 Timing 17 


A1B1 

Critical 

missed 

13 

Event to 


A4B1 

13 

1st level 

missed 

cause links 


A1C1 

diagnosis 

5 

B1E6 



6 

Cause to 


Cause links 

B1N4 Temporary obstruction 6 

to view 

multiple links 5 

Vehicles in bicycles – Time of Day 

Critical 

Events 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 

12-18 (n=35) 18-24 (n=10) 


A4 Distance and 

A5 Speed 

4 A5 Speed 1 

A1B1 

Timing - 


Observation 17 A1B1 

missed 

missed 

6 

A1C1 



8 A1C1 

diagnosis 

diagnosis 

3 

A5D1 

Speed - 


3 A1D1 


plan 

1 

C1J2 


Information failure 6 B1D1 Observation missed - 


2 

B1D1 

Observation 


missed - 3 C1J2 



2 

B1N2 

Observation 

missed - 

Permanent 

obstruction 

view 

to 

3 D1L2 


Insufficient knowledge 

2 


Page 111


Analysis according to the age of participants 

Pedestrians 

“Timing” was the most frequent critical event for all age groups. Missed 

observations and faulty diagnoses occur in all age groups as 1st level causes 

(Table 37). In the oldest age group “faulty diagnosis” is the most frequent 

which indicates for example that older pedestrians have often noticed 

oncoming vehicle but supposed that vehicle would give way. 

Table 37, Most frequent SNACS links for pedestrians according to age 

Pedestrians - Age 

Under 25 years (n=29) 

25-44 (n=13) 


Critical 

A2 / 

Events A4 Distance 3 

Duration / Sequence 2 

A8 

Timing - 

A1B1 

Critical 

Observation missed 11 A1D1 Timing - Inadequate 

4 

plan 

Event to Timing - Faulty 


A1C1 

9 A1C1 

3 

1st level diagnosis 

diagnosis 

cause links Timing - Decision 

A1C3 

4 

error 



B1D1 

7 B1E3 

2 

Cause 

Cause 

links 

to 

B1N4 



- Temporary 



C1J2 


Pedestrians - Age 

6 B1N4 

5 C1J2 

Distraction 

Observation missed- 

Temporary 




2 

2 

Critical 

Events 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 

45-64 (n=20) 65 and over (n=27) 



A1B1 

Timing - 

Observation missed 6 A1C1 Timing - Faulty 

diagnosis 

11 

A1C1 



5 A1B1 

7 

diagnosis 

missed 

A1D1 

Timing - 


3 A1D1 

4 


plan 

C1J2 



3 C1J2 



5 

B1E3 



2 B1E3 

- Distraction 

Distraction 

2 

D1E9 



Psychological 2 C1F2 

Cognitive bias 

stress 

2 


Page 112


Vehicles in pedestrian accidents 

For vehicle drivers in pedestrian accidents “timing” was the most frequent 

critical event and “timing” to “observation missed” the most frequent link for all 

age groups (Table 38). The most frequent cause to cause links led to 

“temporary obstruction to view”. 

Table 38, Most frequent SNACS links for vehicle drivers in pedestrian accidents 

according to age of driver 

Vehicles in pedestrians – Age of Driver 

Critical 

Events 

Critical 

Event to 

1st level 

cause links 

Cause 

Cause 

links 

to 

Under 25 years (n=11) 25-44 (n=37) 


A5 Speed 2 A4 Distance 4 

A1B1 

Timing- 


missed 

16 

A1D1 



2 A1C1 

plan 

diagnosis 

8 

A1C3 

Timing - Decision 

Distance - 

2 A4B1 

error 


3 

B1C1 



2 B1N4 Temporary 



8 

B1N4 


- Temporary 


2 C1J2 


C1J2 

2 B1E3 


Vehicles in pedestrians – Age of Driver 




Distraction 

5 

4 

Critical 

Events 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 




A1B1 

Timing - 

Observation missed 8 A1B1 Timing - 

Observation missed 6 

A1C1 


Distance - 

6 A4B1 

diagnosis 

Observation missed 3 

A4B1 

Distance - 



B1N4 



- Temporary 5 B1C1 



2 

B1G3 


- Temporary sight 

obstruction 

2 B1E6 


- Inattention 

multiple links 1 multiple links 1 

2 


Page 113


Bicycles 

Missed observations and faulty diagnoses are the most frequent 1st level 

cause again. Within cause to cause links in the youngest age group 

“insufficient knowledge” is the most frequent link compared to “information 

failure” in other age groups (Table 39). 

Table 39, Most frequent SNACS links for bicyclists according to age of bicycle driver 

Age of bicyclists 


25-44 (n=18) 


Events A6 Direction 5 A4 Distance 6 

Timing - 

A1B1 

Critical 


diagnosis 

5 

Event to Direction - 

Distance - Faulty 

A6D1 

5 A4C1 

1st level Inadequate plan 

diagnosis 

4 

cause links 

Direction - 

multiple links 2 A6D1 


3 



D1L2 Insufficient 4 C1J2 


to knowledge 

3 

Cause 

Cause 

links 

B1D1 



B1E3 


- Distraction 

Age of bicyclists 

2 B1E3 


Distraction 


2 

Critical 

Events 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 



A2, 

Duration, Distance, 

A4, 

3 

Direction 

A6 

A6 Direction 4 

A1C1 



8 A1B1 

8 

diagnosis 

missed 

A1B1 

Timing - 

Observation missed 6 A1D1 Timing - Inadequate 

8 

plan 

A1D1 

Timing - 

4 A1C1 


4 

C1J2 

B1N4 





- Temporary 


multiple links 2 

5 C1J2 

diagnosis 




3 


Page 114


Vehicles in bicycle accidents 

For vehicle drivers in bicycle accidents “timing” was the most frequent critical 

event for all age groups. “Observation missed” was the most frequent link to 

first level cause for all age groups except for 25 to 44 years old drivers who 

were more often observed as making a “faulty diagnosis” of the situation 

(Table 40). 

Table 40, Most frequent SNACS links for vehicles in bicycle accidents according to age 

of driver 

Age of drivers of the vehicles in bicycle accidents 

Critical 

Events 

Critical 

Event to 

1st level 

cause links 

Cause 

Cause 

links 

to 


25-44 (n=37) 


A5 Speed 4 A4 Distance 12 

A1B1 

Timing - 


diagnosis 

11 

A5D1 

Speed - Inadequate 


3 A1B1 

plan 

missed 

9 

multiple links 2 A4B1 


9 

missed 

C1J2 



5 C1J2 



11 

D1L2 



Insufficient 5 B1E6 


knowledge 

6 

B1C1 



3 B1C1 



Information failure – 


J2N1 Inadequate road 3 B1H5 Permanent sight 

design 

obstruction 

Age of drivers of the vehicles in bicycle accidents 

5 

5 

Critical 

Events 

Critical 

Event to 

1st level 

cause links 

Cause to 

Cause links 




A1B1 

Timing - 


missed 

5 

A4B1 

Distance - 



A1C1 


diagnosis 

3 

B1D1 





B1N2 - Permanent 4 



B1N4 - Temporary 4 



Page 115



There were a total of 180 investigated accidents where a slowly moving 

vulnerable road user was involved. Pedestrians were involved in 87 accidents 

and bicyclists in 93 accidents. 

For pedestrians “Timing” was most frequent critical event, which accounted for 

68 percent of all critical events for pedestrians. Usually action was performed 

prematurely (33 cases) which suggests that the pedestrian stepped on the 

roadway before the opponent vehicles had passed the site. Pedestrians had 

often assumed that car drivers had noticed them. This assumption was 

classified as “Faulty diagnosis” which was a common cause for pedestrians. 



However there were usually no actions performed before the collision (27 

cases). For vehicle drivers, “Missed observations” was a frequent risk factor in 

the accidents. 

Bicycle drivers often had wrong assumptions of other road user’s intentions. 

Bicyclists assumed that car drivers had noticed them and would give way. 

In bicycle accidents, different obstructions to the view contributed to actions or 

lack of actions of opponent vehicle drivers. 

Conclusions from data within the context variable groups (participant age and 

time of day) are more difficult to make, because the number of accidents 

within groups is reduced. There were, for example, only a few night time 

accidents and accidents during weekends. However, some thoughts can be 

expressed. The youngest vulnerable road users had problems in crossing the 

roadways due to inadequate plans (usually pedestrian or bicyclists running or 

driving suddenly onto the road) or insufficient knowledge (not preparing for 

other road users). The oldest age group often supposed that other vehicles 

had noticed them and would give way. 

The aim of this analysis was not to give a totally representative picture of 

slower moving vulnerable road user accidents, but rather to demonstrate the 

potential uses for the accident causation database and identify common 

accident scenarios and areas of interest for future work. 


Page 116


3.5 Aggregated analysis summary 

This section presents a short summary of the analysis made in this report. 

In total 1783 vehicles and pedestrians were part of the analysis. The analyses 

were performed on four groups later divided into subgroups. The groups were: 

• Leaving lane vehicles (n = 354) 

• Catching vehicles (n = 537) 

• Crossing vehicles (n = 528) 

• Slower moving VRUs (n = 92 pedestrians; 95 Pedal Cyclists, 177 

opponents) 

3.5.1 Vehicle leaving its lane 

354 vehicles were assigned to the leaving lane trajectory category. 86% of 

these were classed as having left their lane unintentionally due to loss of 

control and 14% were classified as having left their lane intentionally as part of 

a lane change or overtake manoeuvre. 

The most frequently occurring critical events for leaving lane accidents were 

‘Direction’ (A6) and ‘Speed’ (A5). Travelling too fast leading to a loss of control 

or travelling in the wrong direction would be expected for leaving lane accident 

and are also associated with single vehicle accidents occurring on a rural 

road. These characteristics were found to be prevalent in the leaving lane 

vehicles and this lends validity to these findings. 

The most commonly occurring links between the critical event and first level 

cause for leaving lane vehicles is ‘Direction’ to ‘Inadequate plan’ (A6-D1) and 

‘Speed’ to ‘Inadequate plan’ (A5-D1). This makes ‘Inadequate plan’ (D1) the 

most commonly occurring first level cause for the leaving lane vehicles with a 

35% share. The second most common 1st level cause is ‘Observation missed’ 

(B1) with 18%. ‘Observation missed’ (B1) is linked most frequently with 

‘Direction’ (A6) and the A6-B1 link occurs 57 times. ‘Faulty diagnosis’ (C1) 

also occurs relatively frequently as a 1st level cause (16%). 

Other Common cause to cause links in leaving lane accidents were 

‘Inadequate plan’ to ‘Insufficient knowledge’ and ‘Influence of substances’ 

(D1-L2 43 links; D1-E7 42 links) however these links only occurred together in 

5 leaving lane vehicles. The link chain A5-D1-L2 occurs 21 times suggesting 

that this scenario is a fairly common one for leaving lane vehicles. ‘Information 

failure’ (J2) also appears to be an important cause for leaving lane accidents 

as it accounts for 10% of the first level causes, 6% of the second and also has 

strong links with ‘Faulty diagnosis’ (C1-J2, 25 links) and ‘State of road’ (J2-K5, 

37 links). 

The SNACS charts for vehicles assigned to the leaving lane trajectory reveal 

that there are many causes or factors that contribute to leaving lane 

accidents. They suggest that human factors such as ‘Influence of substances’, 


Page 117


‘Insufficient knowledge’ and ‘Fatigue’ and environmental issues such as the 

‘State of road’ (K5) can lead to cognitive errors such as ‘Faulty diagnosis’, 

‘Inadequate plan’ and ‘Observation missed’ and contribute to critical events 

such as travelling in the wrong direction (Direction A6) or travelling too fast 

(Speed A5). 

3.5.2 Vehicle encountering something in its lane, either in 

front or from the rear 

The vehicle encountering something in its lane analyses were performed on a 

total of 537 vehicles. Due to the variation in the types of accidents included 

this group; results are more meaningful if they are reported by subgroup 

rather than as a whole. The vehicle encountering something in its lane 

subgroups are: Being struck from behind (RF); Striking vehicle in front (FR); 

Being struck by a vehicle which has left its lane (S); and Striking object other 

than vehicle in front (O). The latter group had too few vehicles to draw 

meaningful conclusions so were excluded. 

In the RF subgroup there were 161 vehicles. The results show that the 

standard ‘passive vehicle’ chain has often been used in this subgroup. This is 

normal because this subgroup includes vehicles that were struck from behind 

and in this case the main driver problem is that the driver did not understand 

what was going on because of a missing communication with the other drivers 

(J1) or with the road environment (J2). This use of standard coding results in 

A1 is being the most common critical event and C1 and J2 being common 

causes with strong links between all three. Excluding standard chains, the link 

A3 to D1 (11 links) was among the strongest probably due to inadequate 

planning of a manoeuvre. 

In the FR subgroup there were 149 vehicles. A1 (timing) is the most used 

critical event followed by A4 (distance). The most used first level cause is B1 

(observation missed), followed by C1 (faulty diagnosis) and D1 (inadequate 

plan). A1 has strong links with B1 (45 links) and C1 (29 links). A4 has very 

important links with C1 (30 links) and B1 (24 links). C1, E3 (Distraction), D1 

and E6 (inattention) are the most used last general causes. This means that 

for this subgroup there are attention-distraction and situation comprehension 

driver related problems. 

In S subgroup there are 217 vehicles. Results show that A1 (timing) is the 

most used critical event (72%) and the other critical events used are A5 

(speed) and A6 (direction) both used in 8% of the SNACS. The most used first 

general cause is C1 (55%) followed by B1 (20%) and D1 (17%). A1 has a very 

strong link with C1 (118) and relevant links with B1 (52) and D1 (12 links). A5 

and A6 have good links to D1 (8-10 links). The most frequently used last 

general causes are J2 (30%), C1 (18%), D1 (12%) and J1 (7%). C1 has very 

strong links with J2 (89 links) and J1 (15 links). These results, similarly to RF, 

show a frequent use of standard chains and underline that there are also 

probably problems related to the driver comprehension of the situation for S 

subgroup vehicles. 


Page 118


In the RF and S subgroups there is a frequent use of the standard chains. 

This could be related also to a difficultly of the SNACS to analyse situations in 

which the driver involved in the accident is passive (no action). In contrast in 

the FR subgroup the standard chain is not often used and subsequently has 

the highest number of causes related to an accident. B1 is often used as first 

general cause and other related causes and seems mostly to be related to 

distraction/inattention. The large use of C1, D1, E3 and E6 as the last general 

causes for S and FR subgroups show that there may be problems related to 

the driver’s comprehension of the situation or attention in these two 

subgroups. 

3.5.3 Vehicle encountering another vehicle on crossing paths 

528 vehicles were identified as having crossing path accidents. The most 

common critical event was found to be timing, i.e. a driver takes premature, 

late or no action, followed by speed and distance critical events. This critical 

event occurs more often than all other critical events put together. This is not 

surprising as crossing path accidents occur because one or more vehicle is in 

the wrong place at the wrong time. This is caused by missed observations, 

faulty diagnosis and inadequate plans, i.e. driver behaviour. It is also 

interesting to note that very few crossing paths accidents are caused by 

vehicle malfunctions, further indicating that the study of driver behaviour is an 

important part of reducing accidents. 

For left turn across path conflict scenarios, there is a difference for left-turning 

and straight-going vehicles. For left-turning vehicles, i.e. the vehicle crossing 

the path of another vehicle, the two most common links are A1-B1 (i.e. a 

timing critical event caused by a missed observation) and A1-C1 (i.e. timing 

critical event caused by a faulty diagnosis). This is the same for both 

scenarios Left Turn Across Path-Opposite Direction (LTAP-OD) and Left Turn 

Across Path-Lateral Direction (LTAP-LD). 


the most common links in LTAP-OD scenarios are A1-B1 and A1-C1 but for 

LTAP-LD scenarios the most common ones are A1-C1 and A5-D1. This 

indicates that the there is a difference in causation in these two scenarios, 

with the speed of the straight-going vehicle playing a greater role in LTAP-LD 

crashes. 

For all crossing path accidents, the most common critical event is timing, i.e. a 

driver takes premature, late or no action. The most common general causes 

for this are missed observations or faulty diagnosis of the situation. 

3.5.4 Accidents involving Slower moving Vulnerable Road 

Users 

There were a total of 180 investigated accidents where a slowly moving 

vulnerable road user was involved. Pedestrians were involved in 87 accidents 

and bicyclists in 93 accidents. 


Page 119


For pedestrians “Timing” was most frequent critical event, which accounted for 

68 percent of all critical events for pedestrians. Usually action was performed 

prematurely (33 cases) which suggests that the pedestrian stepped on the 

roadway before the opponent vehicles had passed the site. Pedestrians had 

often assumed that car drivers had noticed them. This assumption was 

classified as “Faulty diagnosis” which was a common cause for pedestrians. 



However there were usually no actions performed before the collision (27 

cases). For vehicle drivers, “missed observations” was a frequent risk factor in 

the accidents. 

Bicycle drivers often had wrong assumptions of other road user’s intentions by 

assuming that car drivers had noticed them and would give way. In bicycle 

accidents, different obstructions to the view contributed to actions or lack of 

actions of opponent vehicle drivers. 


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4 Small scale study comparing cases analysed with 

SNACS and ACASS respectively 

In addition to the aggregated SNACS analysis made in this report a small 

scale comparison between SNACS and the ACASS methods was conducted 

by MUH. 

4.1 Introduction to Accident Causation Analysis with 

Seven Steps – ACASS 

The Accident Causation Analysis with Seven Steps (ACASS) was developed 

to aid the on-scene accident research team, GIDAS (German in-Depth- 

Accident Study) to analyse and collect relevant factors of causes of accidents. 

The methodology includes components from the three areas; human, vehicle 

and environment. 

The system contains a classification of the causes of an accident and is 

divided into three cause factor groups: 

• Group 1, human causation factors 

• Group 2, factors from the technical nature of vehicles and 

• Group 3, factors from the range of the infrastructure and nature. 

Each causation factor group consists of specific categories each of which is 

subdivided into different criterions. Within Group 1 (Human causation factors) 

the criteria are further subdivided into different indicators, see Figure 105. A 

full list of categories, criterions and the indicators can be found in Appendix C. 

Structure of the causation codes -Giving an example from Group 1 (Human factors) 

Group 1 

Human factors 

(1) Information 

access 

(2) Observati on 

(3) Recognition 

(4) Evaluation 

(5) Planning 

(6) Selection 

(7) Opertation 

Seven Steps 

Group 2 

Technical factors 

from the vehicle 

(1) Technical 

defect 

(2) Illegal vehicle 

alteration 

(3) Human-Machine 

Interface 

Group 1: Human factors 

Category 7: Operation 

Criterion: 

(1) Mix-up-error or wrong operation 

(2) reaction error 

Group 3 

Factors from the 

environment and 

the road 

infrastructure 

(1) Condition/ 

Maintenance 

(2) Design of road 

(3) Factor s fr om 

nature 

(4) Other external 

influences 

Indicators: 

(1) Pedals 

(2) Gear shift 

(3) Controls 

• Each group consists of specific categories - 2nd digit of the code 

• Each category consists of specific criterions - 3rd digit of the code 

• Each criterion consits of specific indicators - 4th digit of the code (only within human factors). 

Figure 105, Structure of the causation code (from Jaensch et al., 2008) 


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ACASS is not only a system for recognising and describing causation 

information but also for storing them in a database, by categorising them 

using a system of numeric codes. A system like this requires additional 

information apart from the factors of the cause of the accident in order to be 

able to deliver an as complete picture of the causation of the accident as 

possible. As can be seen in Figure 106, for each accident participant a set of 

codes is collected, which contain information on the causes of the accident 

and the source of the corresponding information as well as their reliability. 

Besides for each causation code an explanatory text is given in a text field. 

Structure of for recording accident causation data with ACASS 

Multiple causation codes for each accident participant are possible : 

... 

x 

x 

Textfield 

Textfield 

Causation factors 

Specification of the factors 

which were identified as 

causes of the accident with a 3 

or 4 digit code from the ranges 

human, machi ne, 

infrastructure/environment 

Source of information of the 

coded accident causes 

Indication of the source of 

information and possibility 

to express doubts 

concerning the reliability of 

the information 

Comment boxes 

to explain the 

selected code 

with a small text. 

Figure 106, Overview over the data to be encoded for ACASS on a case basis (from 

Jaensch et al., 2008) 

In Jaensch et al. (2008) further information about the methodology of ACASS 

can be found. 

4.2 Comparing case analysed with SNACS and ACASS 

respectively 

The following section will give an overview of the small scale study made on 

the comparison of cases analysed with SNACS and ACASS respectively. In 

total 62 accidents; involving 114 road users were included in the study. Three 

partners (MUH, VSRC and DITS) were involved in randomly selecting already 

investigated cases from the existing database. On the selected cases an 

ACASS case analysis was performed. 

The comparison was performed on various types of accidents with no regards 

to vehicle trajectories and do not follow the previously presented analysis in 

section 3. 


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4.2.1 Data analysis of cases analysed with the ACASS method 

71 (62%) of the drivers in the study had at least one causation code (other 

than '00000 - did not contribute to the emergence') and thus have contributed 

to the emergence of the accident. 

These 71 drivers had in total 113 causation codes (on average 1.6 codes per 

participant that contributed to the emergence of the accident) and the 

following table shows the distribution of these 113 causation codes on the 

three Groups of causes: 

Group 1 Human causation factors 104 causes (92%) 

Group 2 Technical factors from the vehicle 3 causes (3%) 

Group 3 Factors from environment and infrastr. 6 causes (5%) 

92% of the causation codes were coded as Group 1 and 8% of the causation 

codes were coded as Group 2 and Group 3. For this reason this study will 

only focus on Group 1. 

Group 1, the Human causation factors are subdivided into 7 categories. These 

describe were an error has occurred in the 7 basic human functions when 

reacting to a situation (7 Steps). Figure 107 gives an overview of how often 

the failure of each of the 7 human basic functions has contributed to the 

emergence of an accident. 

Figure 107, Distribution of failures within the 7 categories of human factors 

About one fourth of the human causation factors of the examined accident 

were errors from the field of ‘Observation’ as well as from the field of 

‘Evaluation’ of the situation. 20% of the human causation factors are 


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connected with the accident participant having problems ‘Recognising’ all the 

important information correctly. Human causation factors from the field of 

‘Selecting’ a correct operation or executing the necessary ‘Operation’ are not 

eminent in any case of the studied sample. 

The frequencies of the criterions of the 7 different Human categories are 

shown in Figure 108. There were 95 codes available with the determination of 

the criteria. The most common criteria coded in the 62 cases were 1-5-02-x 

(Intentional break of rules) 15% of all codes, 1-3-02-x (Wrong focus of 

attention) in 13% of all codes and 1-1-02-x (Information hidden/covered by 

objects outside the vehicle) 11% of the all the codes. Apart from criteria of the 

categories 6 ‘Selection’ and 7 ‘Operation’ also the criteria 1-1-01-x 

(Information not perceivable due to disease or physical condition) from the 

category 1 ‘Information access’ were not eminent in any of the cases. 

Figure 108, Most frequent criteria of the coded cases 

Within the human factors each criterion is further subdivided into specific 

indicators. The frequencies of the 5 most common complete codes are shown 

in the list below: 

5 codes: 1-1-02-9 (Information hidden/covered by objects outside the 

vehicle by multiple objects) 

5 codes: 1-2-01-2 (Distraction from inside the vehicle by passengers) 


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5 codes: 1-3-02-1 (Wrong focus of attention – focus on other road 

user) 

5 codes: 1-3-02-3 (Wrong focus of attention – wrong strategy of 

observation) 

5 codes: 1-4-02-2 (Misjudgement of speed of other road user). 

4.2.2 Data analysis of cases analysed with the SNACS method 

The SNACS method categorises the contributing factors leading to an 

accident. The analysis is performed on the vehicle level and the analysis 

starts by applying a critical event to the vehicle (see section 2.3). In section 3 

the method of superimposing the SNACS charts for the selected group was 

used. The figures presented in this study are rather a count of factors and 

links on a single cause level. 

In this study the aggregation was done without considering the levels of 

confidence for each causal chain. This means that in the final aggregation, a 

low confidence causal chain is attributed the same importance as a causal 


The most common critical event coded in the sample of 113 vehicles are: 

A1 Timing in 70 cases 

A5 Speed in 17 cases 

A6 Direction in 11 cases 

This shows that timing with its specific critical events of “premature action”, 

“late action” or “no action” is the most common results of a causation chain 

which led to an accident. 

The most frequent links from the critical event to the 1st level cause in the 

SNACS chain are: 

A1 to C1 (“Timing” linked to “Faulty diagnosis”) in 37 cases 

A1 to B1 (“Timing” linked to “Observation missed”) in 34 cases. 

and they are significantly more frequent then the other links. The third and 

fourth most frequent link from the critical event to the 1st level cause occur 

about 3 times less frequently than the first and second most frequent link. 

These are: 

A5 to D1 (“Speed” linked to “Inadequate plan”) in 12 cases 

A1 to D1 (“Timing” linked to “Inadequate plan”) in 11 cases. 


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In a third step the most common cause-to-cause links were analysed. The 

results are as follows: 

C1 to J2 

(“Faulty diagnosis” linked to “Information failure”) 

in 31 cases 

B1 to N4 

(“Observation missed” linked to “Temp. view obstruction”) 

in 15 cases. 

B1 to C1 

(“Observation missed” linked to “Faulty diagnosis”) 

in 10 cases. 

4.2.3 Discussion 

The comparison was performed on various types of accidents with no regards 

to vehicle trajectories before the crash e.g. leaving lane or crossing paths. The 

comparison does not follow the previous presented analysis in section 3. 

The result of the SNACS analysis reveals that the most common causes are 

‘Faulty diagnoses’ and ‘Observation missed’. The factors that appear as the 

most common categories when analysing the cases with the ACASS method 

are errors from the field of ‘Observation’ and the field of ‘Evaluation’ which 

matches up the result from the SNACS. 

It should be pointed out that the result of a SNACS analysis is a chart 

illustrating multi-linear sequences of interlinked factors that may contributed to 

the crash event. The original thoughts behind the SNACS method are not to 

compare cause-to-cause links without any regards taken to the whole 

causation chain. However, the study shows some correlation between the 

contributing factors in a selected part of the results from the ACASS codes 

and the SNACS charts. 


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5 General discussion 

The objective of this work package was to develop an in-depth European 

accident causation database to identify risk factors that contribute to road 

accidents. The accident investigations were performed side by side with the 

activities of existing multidisciplinary teams within the partnership which had 

many years of experience. The available resources determined the number of 

accident cases collected and the investigation methodology used. Since 

different data collection methods were used by the investigation teams, the 

variables had to be defined very clearly and these were also revised and 

updated during the project. Training sessions for the teams were performed 

with special emphasis on the SNACS method. 

Even though the teams underwent training, there were still some variations in 

coding and to handle this issue several internal reviews were performed on 

the coding of accidents. It was identified that the training sessions were very 

valuable for the data quality and it is important that such trainings is included 

in future projects. 

Attempting to understand the contributing factors to accident occurrence 

throughout Europe has shown to be a complex task. The new way of thinking 

in accident prevention compared to injury prevention demands the 

understanding of cognitive processes and driver behaviour. Nevertheless, it 

has been shown that when sufficient training has been undertaken and the 

threshold for the understanding of the classification scheme is reached by the 

investigators the results can be considered acceptable. 

The SafetyNet Accident Causation Database contains 1006 accidents 

investigated by teams operating in the six partner countries. 1833 vehicles, 

including pedestrians, were involved in these accidents, the majority of which 

were cars/MPVs (64%). Vehicles were often travelling in urban areas (60%); 

on roads with speed limits in the range 50-90 kph (65%); on straight roads 

(68%) and most vehicles were driven by drivers of an age between 25 and 44 

(36%). Most of the accidents occurred during the day (81%). This will have 

been influenced by the hours which the partners team were able to investigate 

accidents since a number of the teams only operated during the day. 

It is important to notice that the data in the database do not necessarily reflect 

the European Union situation of road accidents. Only six countries contributed 

to the data collection and, as mentioned earlier, due to available resources the 

sample from each partner varied. The emphasis in the work package was to 

identify risk factors leading to accidents and this was achieved by developing 

a European method to guide the investigators in identifying the accident 

contributing factors and developing the accident causation database. 

The main focus of this report was on the aggregated analysis of the cases 

analysed with the SNACS method. Approximately 50 vehicles lacked a 

SNACS analysis therefore they were excluded from the analysis. This was 

mainly due to a lack of information from the data collection, for example the 

drivers in some cases were not interviewed. The main problems found were 


Page 127


that it was sometimes hard to locate the driver or that the driver did not want 

to participate in the study. 

The aggregated analysis was performed on the vehicle level rather than on 

the accident level and it was based on context and vehicle trajectory. The 

accidents involving slower moving vulnerable road users (SVRU) were 

analysed in a separate group because it was believed that these accidents 

would have different causation patterns compared to accidents involving 

solely motorised vehicles. The remaining vehicles were divided into three 

trajectory based groups because it was hypothesised that these groups would 

present the clearest differences in causation patterns. The groups were; 

Vehicle leaving its lane, Vehicle encountering something in its own lane and 

Vehicle encountering another vehicle on crossing paths. 

The SNACS charts in the groups were aggregated to allow the most 

commonly occurring accident contributing factors to be identified. It is believed 

that the aggregation of each analysis group, by describing the frequency of 

accident contributing factors and their relationship, identifies the main 

determiners for how and why an accident occurs in sufficient detail to be used 

for further traffic safety development. For complete analysis and discussion on 

each analysis group, see section 3. 

The aim of the analyses conducted was not to explore and evaluate the 

effectiveness of new technologies, but rather to demonstrate the potential 

uses for the accident causation database and to identify common accident 

scenarios. As shown by the SNACS charts, the information is rich and 

detailed. It is by nature complex as it reflects the complex interactions 

between the road users, vehicles and environment that occur during an 

accident. The SNACS method assists in the process of identifying patterns 

that will allow the most common causes to be focused on when designing 

countermeasures. 

The data from the accident causation study are required for a variety of 

reasons. For example, the data has the potential to allow the monitoring and 

evaluation of vehicle and infrastructure technology, as well as the interaction 

between these and the driver. It is intended that the data can also be used in 

the development of new in-vehicle technology for example accident avoidance 

systems and road design. As the EU grows, with the addition of new Member 

States, the need for accident data to support policy making and road safety 

strategies increases. This will only be achievable within a framework 

facilitating large scale data collection from independent in-depth accident 

investigations conducted in several Member States. 


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6 Conclusions 

• A scientifically supported method (SNACS 1.2) for classification of 

accident contributing factors was developed and evaluated within the 

project. The method was reviewed which resulted in a updated version 

named DREAM 3.0 (Wallén Warner et al., 2008) which can be 

considered as a (the) European method for future in-depth data 

collection activities. 

• Understanding accident causation is a complex task but when sufficient 

training has been undertaken and the threshold for the understanding 

of the protocols and analysis methods is reached by the investigators 

the data quality can be considered acceptable. 

• Due to available resources the accident sample from each partner 

varied. However, each variable and value was coded into the database 

according to a harmonised protocol. 

• It is believed that the aggregation of the data identifies the main 

determiners for how and why an accident occurs in sufficient detail to 

be used for further traffic safety development. However, the use of the 

results reported here is somewhat limited since the sampling in each 

partner Member State was not necessarily representative of the 

national situation and exposure data was not taken into account. 


Page 129


7 References 

European Road Safety Observatory (ERSO). SafetyNet Project, 

Available at http://www.erso.eu/safetynet/content/safetynet.htm 

European Communities, (2001). WHITE PAPER "European transport policy 

for 2010 : Time to decide", Luxembourg: Office for Official Publications of the 

European Communities, Italy 

Fagerlind H., Björkman K., Wallén Warner H., Ljung Aust M., Sandin J., Morris 

A., Talbot R., Danton R., Giustiniani G., Shingo Usami D., Parkkari K., 

Jaensch M., Verschragen E. (2008). Development of an In-depth European 

Accident Causation Database and Driving Reliability and Error Analysis 

Method, DREAM 3, Paper submitted for publication 

Jaensch M., Otte D., Pund B., Chiellino U. Hoppe M. (2008). Implementation 

of ACASS – Accident Causation Analysis with Seven Steps – in In-Depth 

Accident Study GIDAS, Paper submitted for publication 

Najm WG, Smith JD and Smith LD (2001). Analysis of crossing path crashes, 

DOT HS 809 423 

Paulsson R. and Fagerlind H (2006). Review of the accident causation pilot 

study in Task 5.2 Deliverable 5.4 of the EU FP6 project SafetyNet, TREN-04- 

FP6TRSI2.395465/ 506723 

Peden MM, Krug E, Mohan D, Hyder A., Norton R., MacKay M., Dora C. 

(2001). Five-year WHO Strategy on Road Traffic Injury Prevention. Geneva: 

World Health Organization, Ref: WHO/NMH/VIP/01.03. 

Reed S. and Morris A. (2008). Glossary of Data Variables for Fatal and 

Accident causation databases. Deliverable 5.5 of the EU FP6 project 

SafetyNet, TREN-04-FP6TRSI2.395465/ 506723 

Sandin J. (2008). Aggregated Case Studies of Vehicle Crashes by Means of 

Causation Charts, Department of Applied Mechanics, Chalmers University of 

Technology, Göteborg 

Wallén Warner H., Ljung Aust M., Sandin J., Johansson E., Björklund G. 

(2008). Manual for DREAM 3.0, Driving Reliability and Error Analysis Method. 

Deliverable 5.6 of the EU FP6 project SafetyNet, TREN-04- 

FP6TRSI2.395465/ 506723 


Page 130

APPENDIX A: Linking table with glossary for Phenotypes (Critical events) and Genotypes (Causes) 

APPENDIX A: LINKING TABLE WITH GLOSSARY FOR PHENOTYPES (CRITICAL 

EVENTS) AND GENOTYPES (CAUSES) 

PHENOTYPES (A) 

ANTECEDENTS 

(REASONS/CAUSES) 

CONSEQUENTS (RESULTS/EFFECTS) 

GENERAL Genotypes 

Definition of GENERAL 

Phenotypes (Critical events) 

Definitions of SPECIFIC 

Phenotypes (critical events) 

Examples for SPECIFIC Phenotypes 

Observation missed (B1) 

False observation (B2) 

Faulty diagnosis (C1) 

Decision error (C3) 

Inadequate plan (D1) 

Inattention (E6) 

Communication failure - between 

drivers (J1) 

Information failure - between driver 

and traffic environment or driver and 

vehicle (J2) 






Equipment failure (I1) 


drivers (J1) 



vehicle (J2) 




Timing (A1) 

The regulation of time for actions to 

occur. 

Duration (A2) 

Continuance or persistence in time, of 

an action. 

Force/(power) (A3) 

The capacity of an action being 

performed. 

Premature action (A1.1) 

An action started too early, before a 

signal was given or the required 

conditions had been established. 

Late action (A1.2) 

An action started too late. 

No action (A1.3) 

An action that was not done at all 

(within the time interval allowed). 

Prolonged action/movement (A2.1) 

A manoeuvre continues beyond the 

point when it should have been 

terminated. 

Shortened action/movement (A2.2) 

A manoeuvre is interrupted. 

Insufficient force (A3.1) 

Insufficient ability to brake/accelerate. 

Insufficient engine power. 

Performing an overtake before there is 

good visibility. 

Starting/stopping too early at traffic lights. 

Dip the lights too early when driving in the 

dark. 

Not changing lanes in time. 

Starting an overtake too late. 

Dip the lights too late when driving in the 

dark. 

Staying in the left lane too long after 

having performed an overtake. 

Squeezing in just in front of a vehicle 

which one has just been overtaking. 

Not completing braking at stop signs. 

The brakes are not efficient enough. 

The acceleration ability is not enough to 

perform a safe overtake. 

Taken from SNACS Manual 2.1 in Glossary of Data Variables for Fatal and Accident causation databases (Reed and Morris, 2008) 

1

Fear (E2) 




drivers (J1) 



vehicle (J2) 



Wrong reasoning (C2) 






drivers (J1) 



vehicle (J2) 





Distraction (E3) 

Performance Variability (E5) 



drivers (J1) 



vehicle (J2) 


Distance (A4) 

The extent of space between objects or 

places. 

Speed (A5) 

Rate of motion. 

Surplus force (A3.2) 

Too powerful braking/acceleration. 

Too powerful engine. 

Prolonged distance (A4.1) 

A movement taken too far. The vehicle 

is too far from object, destination, or 

intended position. 

Shortened distance (A4.2) 

A movement not taken far enough. The 

vehicle is too close to object, 

destination, or intended position. 

Surplus speed (A5.1) 

Action/manoeuvre performed too 

quickly or with too much speed or 

ended too early. 

Insufficient speed (A5.2) 

Action/manoeuvre performed too 

slowly or with too little speed. 

Acceleration is so strong that one easily 

looses control over the vehicle. 

(Parking too far away from the pavement.) 

The driver was following too close to 

objects in the traffic environment, e.g. a 

vehicle in front. 

Driving cross stop lines and central lines. 

Driving too close to the pavement when 

parking. 

Speeding with regards to speed limit or 

other road users. Skidding in a curve. 

Keeping to low speed when overtaking and 

having to abort the action. 


2


Faulty diagnosis (C1 


Priority error (D2) 

Fear (E2) 





drivers (J1) 



vehicle (J2) 

Inadequate roadside design (N5) 


Wrong identification (B3) 



Performance variability (E5) 


Functional impairment (F1) 

Access problems (H3) 


drivers (J1) 



vehicle (J2) 






Memory failure (E1) 


Access limitations (G1) 


drivers (J1) 




Direction (A6) 

The way in which the vehicle is going. 

Object (A7) 

An item or an actuator. 

Sequence (A8) 

The order in or when/how the event 

takes place/happens. 

Incorrect direction (A6.1) 

Manoeuvre made in the wrong 

direction. 

Adjacent object (A7.1) 

An object which is in physical 

proximity to the object that should 

have been used. 

Similar object (A7.2) 

An object which is similar in 

appearance to the object that should 

have been used. 

Skipped action (A8.1) 

One or more actions of a series of 

actions were skipped. 

Repeated action (A8.2) 

The previous action is repeated. 

Reversed action (A8.3) 

The order of two neighbouring actions 

is reversed. 

Extraneous action (A8.4) 

An extraneous or irrelevant action is 

carried out. 

Turning right instead of left. Going 

backwards instead of forwards. Going off 

the road instead of following the lane. 

The driver hits the brake-pedal instead of 

the accelerator. The driver pushes buttons 

belonging to the climate control instead of 

the radio. 

Activating headlights instead of 

windscreen wipers. 

Changing lanes without checking rearview 

mirrors or looking in the dead angle. 

Looking out for vehicles behind several 

times before changing lanes. 

Changing lane and then indicating 

direction. Turning and then indicating 

direction. 

Braking when not necessary. 


3

vehicle (J2) 



4


OBSERVATION (B) 

ANTECEDENTS (REASONS/CAUSES) 

GENERAL Genotypes SPECIFIC Genotypes (with definitions) Examples for SPECIFIC Genotypes 




Fatigue (E4) 


Under the influence of substances (E7) 


Temporary sight obstruction (G3) 

Permanent sight obstruction (H5) 


Inadequate road design (N1) 

Permanent obstruction to view (N2) 

Temporary obstruction to view (N4) 



Fatigue (E4) 



Physiological stress (E8) 

Psychological stress (E9) 


Glare (B1.1) 

Being faced with bright lights which make it 

difficult to see. 

Noise (B1.2) 

Being surrounded by loud noise which 

prevents perception of other acoustic signals 

Tunnel vision (B1.3) 

Being limited in the peripheral vision. 

Other (B1.4) 


Habit/expectation (B3.1) 


Being used to a certain environment makes it 

Incorrect information (G2) 

difficult to discover changes. 

Mislabeling (H4) 

Information failure - between driver and 

traffic environment or driver and vehicle (J2) Other (B3.2) 

Low sun shining right at the vehicle/person. 

High volume on the stereo keeps one from 

hearing other road users honk the horn. 

When experiencing fear or high speed, the 

peripheral vision diminishes from 180 degrees 

to as much as 20-30 degrees. 

Other (B2.1) A car which is standing still or moving very 

slowly is mistakenly observed as a (faster) 

moving car. 

Signs which have been changed is not 

observed. A sign indicating that what has been 

a primary road for ten years, is hard to notice 

for people who have been driving on that road 

for many years. 

CONSEQUENTS 

(RESULTS/EFFECTS) 

GENERAL Genotypes (with 

definitions) 


A signal or an event that should have been the 

start of an action (sequence) is missed, i. e., 

not seen, not heard, not felt etc.. 


An event or some information is incorrectly 

recognised or mistaken for something else. 

Wrong identification(B3) 

The identification of an event or some 

information is incorrect. 


5


INTERPRETATION (C) 








Cognitive bias (F2) 



Communication failure (between drivers) (J1) 


traffic environment or driver and vehicle (J2) 





Error in mental model (C1.1) 

The individual’s ideas on a place or turn of 

events does not correspond to reality. 

New situation (C1.2) 

The individual ends up in a completely new 

situation and has no frame of reference for 

making a judgement call. 

Incorrect analogy/comparison (C1.3) 

The drivers understanding is based on a 

metaphor or an analogy which has no 

correspondence in the real world. 

Misjudgement of time/distance (C1.4) 

The drivers estimation of distance or time is 

not correct. 

Other (C1.5) 

Incorrect analogy/comparison (C2.1) 

The drivers understanding is based on a 

metaphor or an analogy which, in reality, has 

no correspondence. 

Error in mental model (C2.2) 



The driver believes making a left turn is 

allowed, but going left is prohibited. 

Driving on a road in the country and all of a 

sudden a sheep appears on the road. 

Car A reaches an intersection slightly ahead 

of car B. Car B has right of way but slows 

down. The driver of car A believes that driver 

B is being nice and wants A to pass the 

intersection first despite B’s right of way. In 

reality B is slowing down due to a speed bump 

just prior to the intersection, and have no 

intention to let A pass first. 

Initiates a left turn before opposite traffic have 

passed. 

Car A reaches an intersection slightly ahead of 

car B. Car B has right of way but slows down. 

The driver of car A believes that driver B is 

being nice and wants A to pass the intersection 

first despite B’s right of way. In reality B is 

slowing down due to a speed bump just prior 

to the intersection, and has no intention to let 

A pass first. 



CONSEQUENTS 



definitions) 


The diagnosis of the situation is incomplete or 

incorrect. 


Concluding something based on assumptions. 


6



Fear (E2) 








Insufficient skills (L1) 

Insufficient knowledge (L2) 


Other (C2.3) 

Shock (C3.1) 

The driver is in a state of shock. 

Other (C3.2) 

The driver is in a state of shock because of the 

situation. 


Coming to an incorrect decision due to 

inability of making the right choice among 

many decisions, or inability of making any 

choice at all. 

GENERAL Genotypes 



Fear (E2) 


Fatigue (E4) 




Insufficient experience (L2) 

Deficient instructions/procedures (M1) 

Overload / Too high demands (M2) 





Communication failure - between drivers (J1) 



SPECIFIC Genotypes (with 

definitions) 

Error in mental model (D1.1) 



Overlooked side effects (D1.2) 

The driver does not realise that his/her 

action will have side effects which will 

have a negative influence on the situation. 

Other (D1.3) 

Legitimate higher priority (D2.1) 

One action is legitimately prioritised 

compared to another. 

Conflicting criterions (D2.2) 

The driver needs to solve two contradictory 

tasks at the same time. 

PLANNING (D) 

Examples for SPECIFIC Genotypes 



The driver realises that the traffic lights is 

turning red, and surprises vehicle coming 

from behind by braking very hard. 

Altering lane in a less appropriate way in 

order to let an ambulance pass. 

Listening to traffic information on the radio 

while at the same time talking on the mobile 

phone. 

CONSEQUENTS 


GENERAL Genotypes (with definitions) 


The plan is not complete, or wrong, i.e. it does not 

contain all the details needed when it is carried out. 


Not making the correct priorities and the plan will 

therefore not be effective. 


7


traffic environment or driver and vehicle (J2) Other (D2.3) 

Deficient instructions/procedures (M1) 

TEMPORARY PERSONAL FACTORS (E) 


CONSEQUENTS (RESULTS/EFFECTS) 

GENERAL Genotypes SPECIFIC Genotypes (with definitions) Examples for SPECIFIC Genotypes GENERAL Genotypes (with definitions) 

Overload / Too high demands 

(M2) 

None defined 


Learning long ago (E1.1) 

It has been several years since the 

learning/training took place. 

Temporary inability (E1.2) 

The individual cannot, at that moment, handle 

something which normally is not a problem. 

Other (E1.3) 

Previous mistakes (E2.1) 

One has previously made mistakes in similar 

situations and fears to repeat them. 

Insecurity (E2.2) 

The driver doubts his/her own ability of handling 

the situation. 

Conceivable consequences (E2.3) 

One becomes scared when realizing which 

consequences the current situation might have. 

Other (E2.4) 

Passengers (E3.1) 

Another person in the vehicle diverts the driver's 

attention. 

External competing activity (E3.2) 

An object or a sequence of events outside the 

vehicle diverts the driver's attention. Paying 

attention to this object or sequence of events 

could be part of the whole driving task but 

competing with the task concerned. 

Encounter a traffic situation which one has not 

been in for many years. 

An item or some information cannot be recalled 

when needed, i.e. due to bad short-term 

memory. 

Anxious about a particular manoeuvre due to 

previous bad experience/accident. 

Truck driving in the opposite direction enters 

the wrong side of the central line, some 

distance away. 

Conversations with co-passengers, children 

fighting etc. 

An animal appearing by the side of the road. 


An item or a piece of information cannot be recalled 

when needed. 

Fear (E2) 

Being afraid of something. 


The performance of a task is suspended because the 

person's attention was caught by something else or the 

attention has shifted. 


8


(M2) 

Management failure (M3) 

Insufficient skills (L1) 



(M2) 


None defined 


Internal competing activity (E3.3) 

The mobile phone ringing, the navigation 

An object or a sequence of events inside the system alerting or the road user is thinking of 

vehicle diverts the driver's attention. Paying something in particular. 

attention to this object or sequence of events 

could be part of the whole driving task but 

competing with the task concerned. 

Other (E3.4) 

Circadian rhythm (E4.1) 

Driving at a time which is normally not within the 

"waking hours" and that results in reduced output 

capacity. 

Extensive driving spell (E4.2) 

Not taking breaks or pausing when driving long 

distances, and that leads to diminished driving 

ability. 

Other (E4.3) 

Illness (E5.1) 

The individual is struck with a condition of 

illness which affects the ability to drive in a 

negative way. 

Other (E5.2) 

Temporary inability (E6.1) 



Bored/unmotivated (E6.2) 

The individual lacks motivation to carry out 

his/her task in the best way possible. 

Habit/expectation (E6.3) 

Being used to a certain environment makes it 

difficult to discover changes. 

Other (E6.4) 

Alcohol (E7.1) 

The road user is under the influence of alcohol. 

Driving at night to avoid heavy traffic. 

Truck drivers changing trucks with each other 

and driving more than the allowed period of 

time during 24 h. 

Have a heart attack, suffer from dizziness, 

feeling nauseous, etc 

The driver of a car starts coughing very much 

and is not able to pay attention to the driving. 

Driving the same distance to work every day. 

Signs which have been changed are not 

observed. A sign indicating that what has been 

a primary road for ten years, is hard to notice 

for people who have been driving on that road 

for many years. 

A vehicle goes off the road because the driver 

had been drinking. 

Fatigue (E4) 

Being mentally or physically tired/exhausted. 

Performance variability (E5) 

Reduced or increased precision of actions. 


Low vigilance due to loss of focus. 


Being affected by different sorts of substances. 


9


(M2) 


Information failure - between 

driver and traffic environment 

or driver and vehicle (J2) 



(M2) 



Drugs (E7.2) 

A vehicle is going off the road because the 

The road user is under the influence of nonprescribed 

driver had been injecting heroin. 

drugs. 

Medication (E7.3) 

The road user is under the influence of prescribed 

drugs. 

Other (E7.4) 

Illness (E8.1) 

The individual is struck with a condition of 

illness which negatively affects the ability to 

drive. 

Other (E8.2) 

A vehicle is going off the road because the 

driver had been taking strong sedatives. 

Have a heart attack, suffer from dizziness, 

feeling nauseous, etc. 


Different physical factors putting a strain on the driver. 

Other (E9.1) Psychological stress (E9) 

Different mental factors putting a strain on the driver. 

PERMANENT PERSONAL FACTORS (F) 


CONSEQUENTS 




definitions) 

None defined Other (F1.1) Functional impairment (F1) 

Reduced ability in one or more human 

functions. 


10


None defined Other (F2.1) Cognitive bias (F2) 

Taking in and processing information a little 

bit askew. 


11


TEMPORARY HMI PROBLEMS (G) 


CONSEQUENTS 


GENERAL Genotypes SPECIFIC Genotypes (with definitions) Examples for SPECIFIC Genotypes GENERAL Genotypes (with 

definitions) 


Software fault (I2) 

Temporary inability (G1.1) 



The driver has a temporary blackout and has 

forgotten how to handle either the situation, 

the car or both of them. 

Access limitations (G1) 

Problems for the user to reach items/actuators 

in the driver environment. 





None defined 

Other (G1.2) 

Badly presented display (G2.1) 

The display does not show the information in 

the intended/correct way. 

Navigation problems (G2.2) 

Difficulties to navigate within the information 

systems. 

Other (G2.3) 

Baggage (G3.1) 

Some kind of baggage or similar object is 

placed in such a way that it obstructs the 

drivers view. 

Passengers (G3.2) 

One or more passengers are placed in such a 

way that they block the view the driver 

normally has. 

Other (G3.3) 

The interface of a GPS-display is not 

optimized and the driver has a hard time 

interpreting the information given. 

The menu in the navigation system is difficult 

to understand, and the driver needs to pay a lot 

of attention to the same. 

Too much luggage in the car and the driver's 

field of vision is completely or partially 

blocked when looking in the rear-view mirror. 

A very tall person is seated in position 2:2 (in 

the middle of the back seat) which makes it 

difficult for the driver to see, in the rear-view 

mirror, what is going on behind the car. 


Information is being ambiguously, 

incompletely or incorrectly 

formulated/presented. 

Temporary sight obstruction (G3) 

The view is temporarily obstructed by an 

object. 


12


PERMANENT HMI PROBLEMS (H) 


CONSEQUENTS 




definitions) 

Inadequate HMI (O2) 

Inadequate ergonomics (O3) 

Other (H1.1) Sound (H1) 

Noise levels are too high or signal levels are 

too low. 



Maintenance failure - condition of vehicle 

(K1) 



Inadequate quality control – vehicle (K3) 


Other (H2.1) Illumination (H2) 

Being exposed to too much light, e.g. causing 

reflexes, glare, or not having enough light e.g. 

causing reduced colour, contrast. 

Other (H3.1) Access problems (H3) 

An item or an actuator is in one way or 

another out of reach to the user. 

Incorrect translations (misleading terms in 

manuals etc) (H4.1) 

Translation of hand books and such is poor. 

Other (H4.2) 

Ambiguous terms used in the manual. 

Mislabeling (H4) 

The labeling or identification of an item or 

actuator is incorrect or ambiguous. 

Inadequate ergonomics (O3) Other (H5.1) Permanent sight obstruction (H5) 

The sight is permanently obstructed due to the 

vehicle design. 


13


EQUIPMENT FAILURE (I) 




(K1) 

Maintenance failure - condition of road (K2) 

Inadequate quality control – vehicle (K3) 

Unpredictable system functions/characteristics 

(O1) 

Inadequate construction (O5) 

Inadequate quality control - vehicle (K3) 

Tyres (I1.1) 

One or many tyres fail to maintain pressure, 

thereby not performing as expected. 

Steering (I1.2) 

The steering system fails in, one way or 

another, and does not perform as expected. 

Brake system (I1.3) 

The brake system fails, in one way or another, 

and does not perform as expected. 

Lighting (I1.4) 

The lighting fails, in one way or another, and 

does not perform as expected. 

Other (I1.5) 

Deficient navigation system (I2.1) 

Information is not available due to software 

problems or other such problems. 

Other (I2.2) 

A tyre explodes. 

The steering column breaks. 

A brake-disc is overheated. 

The left front headlight is not working. 

The performance of the system slows down. 

This can be critical for command and control, 

in particular. 

CONSEQUENTS 



definitions) 


Some piece of equipment brakes or the 

performance of a system does not behave as 

expected/intended. 


The software is performing slower than 

expected or not at all. 


14


COMMUNICATION (J) 












Inadequate design of communication devices 

(O4) 

Noise/music (J1.1) 

Being surrounded by loud noise or music 

which prevents perception of other acoustic 

signals. 

Temporary inability (J1.2) 



Glare (J1.3) 

Being faced with bright lights which make it 


Other (J1.4) 

Sound (H1) 

Noise (J2.1) 

Illumination (H2) 

Being surrounded by loud noise which 


prevents perception of other acoustic signals. 

Maintenance failure - condition of road (K2) Glare (J2.3) 

Inadequate quality control - road (K4) Being faced with bright lights which make it 

State of road (K5) 



Information overload (J2.3) 


Too much information being conveyed to the 

Inadequate information design (temporary or 

road user. 

permanent) (N3) 


Design of traffic flows (N6) Other (J2.4) 


hearing other road users, for instance, honk the 

horn. 



hearing other road users signaling by using the 

horn. 


Too many signs, both commercial and noncommercial, 

by the road, which makes it 

difficult to select which pieces of information 

it is the most important to pay attention to. 

CONSEQUENTS 



definitions) 

Communication failure (between drivers) 

(J1) 

A message or a transmission of information 

failed to come through to the receiver (another 

road user). 


traffic environment or driver and vehicle 

(J2) 

A message or a transmission of information 

failed to come through to the receiver (the 

road user). 


15

None defined 

None defined 


MAINTENANCE (K) 


CONSEQUENTS 


GENERAL Genotypes SPECIFIC Genotypes (with definitions) Examples for SPECIFIC Genotypes GENERAL Genotypes (with definitions) 

Tyres (K1.1) 

One or many tyres have been inadequately 

maintained or checked and does not perform 

as expected. 

Steering (K1.2) 

The steering system has been inadequately 


as expected. 

Brake system (K1.3) 

The brake system has been inadequately 


as expected. 

Lighting (K1.4) 

The lighting has been inadequately maintained 

or checked and does not perform as expected. 

Other (K1.5) 

Inadequate road markings (K2.1) 

Markings in the road surface are hardly visible 

or non existing. 

Road (surface) in poor condition (K2.2) 

The condition of the road surface is sub 

standard. 

Road surface covered (K2.3) 

The surface of the road is covered by 

something that impedes driving performance. 

Other (K2.4) 

A tyre explodes because it has been worn out. 

The level of servo oil is to low. 

The brake-blocks have not been replaced in a 

long time. 

A non-functioning brake light has not been 

replaced. 

Painted arrows in the road surface indicating 

which way the lanes are going, have been 

worn out. 

The road is full of holes or the road surface 

needs re-paving since too many cars have been 

going on studded tyres. 

The road surface is covered with snow, oil etc. 


(K1) 

The vehicle, or parts of the equipment, is out 

of order due to inadequate or incorrect 

maintenance. 

Maintenance failure - condition of road 

(K2) 

The road or parts of the road is in a poor state 

due to inadequate or incorrect maintenance. 


16


None defined Other (K3.1) Inadequate quality control - vehicle (K3) 

The vehicle, or parts of the equipment, has not 

been subject to adequate quality control by the 

responsible party, e.g. the user. 

None defined 

None defined 

Poor choice of road surface (K4.1) 

The surface chosen when the road was being 

built is not up to standard. 

Inadequate planning (K4.2) 

Inadequate routines for maintenance of roads 

which are supposed to keep a safe and 

functional level of standard. 

Other (K4.3) 

Change of road surface friction (K5.1) 

The friction in the road surface is changed due 

to different factors. 

The asphalt on the road is of poor quality and 

the road surface is decomposited. 

The road surface has in time decomposited. 

After the snow plough has been ploughing 

there is often a little bit of snow left which 

reduces the road friction. 

Rain falling after having had a long period of 

drought makes the road slippery when oil and 

dirt comes up and forms a thin layer at the top 

of the surface. 

Inadequate quality control - road (K4) 

The road or parts of the road has not been 

subject to adequate quality control by the 

responsible party, e.g. the road administration. 

State of road (K5) 

The current road-holding characteristics. 

EXPERIENCE / KNOWLEDGE (L) 


CONSEQUENTS 




definitions) 

Inadequate training (M4) Other (L1.1) Insufficient skills (L1) 

Lack of practical experience to handle i.e.; a 

task, an activity, piece of equipment etc. 


17


Inadequate training (M4) Other (L2.1) Insufficient knowledge (L2) 

Lack of knowledge due to unawareness, 

confusion etc. 

ORGANISATION (M) 


CONSEQUENTS 




definitions) 

None defined Other (M1.1) Deficient instructions/procedures (M1) 

Instructions or descriptions of procedures are 

either incomplete, ambiguous, unsuitable or 

incorrect. 

None defined Other (M2.1) Overload/ Too high demands (M2) 

The road user is subjected to too much 

pressure or stress. 

None defined Other (M3.1) Management failure (M3) 

The planning and/or the management of work 

or working conditions is inadequate. 

None defined Other (M4.1) Inadequate training (M4) 

The user has not been trained well enough. 


18


None defined 

None defined 

None defined 

ROAD DESIGN (N) 



Optical guidance (N1.1) 

The visual guidance (in most cases painted 

marks) of the road is not sufficient. 

Vertical alignment (N1.2) 

The road is built in a very hilly environment. 

Horizontal alignment (N1.3) 

The road is built in a very winding 

environment. 

Design of cross section (N1.4) 

The cross section is not well-considered 

enough. 

Other (N1.5) 

Vegetation (N2.1) 

The view is completely or partly blocked by 

vegetation. 

Building/fence (N2.2) 


buildings or fences. 

Signs (N2.3) 


one or more signs. 

Other (N2.4) 

Unclear route information (N3.1) 

The design of the route information makes it 

difficult for the driver to scan the situation. 

No central line to tell which way the road is 

turning in the distance ahead. 

Too many hills which makes it difficult to see 

the distance ahead. 

Too many curves which makes it difficult to 

look and plan ahead. 

The camber is inadequately designed in a 

curve. 

High hedges and bushes which reduces the 

visibility. 

A high fence in a residential area which 

reduces the view when going round a corner. 

A commercial sign by the side of the road 

blocking the view in an intersection. 

Several possible routes are stated on one sign 

post and if one is new to the place and needs 

to read carefully to know which way to take, a 

lot of attention needs to be paid to that sign 

post which makes it hard to concentrate on the 

driving and the surrounding traffic. 

CONSEQUENTS 



definitions) 


The planning and construction of the road is 

insufficient. 


Objects in traffic the environment causing 

permanently reduced visibility. 

Inadequate information design 

(temporary or permanent) (N3) 

The design of the traffic control or traffic 

guidance is not adequate. 


19

None defined 

None defined 


Too many traffic signs (N3.2) 

Several traffic signs placed within a close 

range. 

Inappropriate placement of traffic lights 

(N3.3) 

The traffic lights placed in a way which makes 

it hard to follow them. 

Inappropriate placement of traffic signs 

(N3.4) 

The traffic signs are placed in a way which 

makes it hard to read/follow them. 

Other (N3.5) 

Weather conditions (N4.1) 

The view is completely or partly blocked 

because of the weather conditions. 

Other vehicle (N4.2) 


another vehicle. 

Other (N4.3) 

Placement of road equipment (N5.1) 

Objects placed in the proximity of the road, 

e.g. energy absorbing structures. 

Placement of objects in roadside (N5.2) 

Objects placed in a less appropriate way, in 

the proximity of the road. 

A large number of traffic signs within close 

proximity makes it difficult to know which 

one to follow. 

Standing first in line at a traffic light and not 

being able to see the lights because they are 

located almost right above ones vehicle. 

A traffic sign is placed too close to a cross 

section and the driver is forced to take quick 

action which might surprise the fellow road 

users. 

A lot of snow or rain is falling, or it might be 

very foggy, and each of these conditions 

makes it hard for the road user to see what is 

happening in the distance. 

Another vehicle passes by and blocks the 

view. 

An energy absorbing terminal is located too 

close to the driving lane. 

An avenue of trees which have been planted 

alongside a road. 


Objects in traffic the environment causing 

temporarily reduced visibility. 


The planning and construction of the roadside 

is insufficient. 

Design of cross section (N5.3) 

The cross section has not been planned well 

enough. 

Other (N5.4) 

None defined Other (N6.1) Design of traffic flows (N6) 

The arrangement of, e.g. lanes, is a source of 

confusion. 


20

None defined 


VEHICLE DESIGN (O) 



Load (O1.1) 

A certain amount of load makes the vehicle 

behave unpredictably. 

Other (O1.2) 

If one is driving with a lot of baggage in the 

trunk and enters a curve with too much speed, 

the car might become under steered and go off 

the road. 

CONSEQUENTS 



definitions) 

Unpredictable system 

functions/characteristics (O1) 

The characteristics of the vehicle become 

unpredictable under some circumstances. 

None defined Other (O2.1) Inadequate HMI (O2) 

The interaction between user and an in-vehicle 

system is inadequately designed. 

None defined Other (O3.1) Inadequate ergonomics (O3) 

The driver seat, for instance, is inadequately 

designed from an ergonomic point of view. 

None defined Other (O4.1) Inadequate design of communication 

devices (O4) 

The vehicle's light signals (indicators, brake 

light, head lights, reverse lights) are unable to 

communicate in situations when necessary. 

None defined 

Tyres (O5.1) 

The tyres have been inadequately constructed 

and does not perform as expected. 

Steering (O5.2) 

The steering system has been inadequately 

constructed and does not perform as expected. 

Brake system (O5.3) 

The brake system has been inadequately 


Lighting (O5.4) 

The lighting has been inadequately 


Other (O5.5) 

The design of tyres makes the vehicle 

aquaplaning. 

The driver looses control of the vehicle 

because turning the steering wheel has no 

effect. 

The brakes are all rusty because of exposure to 

water and yield almost no braking power. 

The front headlights produce insufficient light. 

Inadequate construction (O5) 

The vehicle has been insufficiently built or the 

construction has been insufficiently 

considered. 


21

APPENDIX B 

How to sort the accidents 

• Sort the accidents by using the GDV‐codes 

– Accidents where a vehicle leaves its lane 

(When a vehicle crossed the median and collided with an oncoming vehicle, the oncoming vehicle 

was sorted as an “a vehicle encounter something in its own lane”) 

Yellow 

– Accidents where a vehicle encounters something in its lane 

Green 

– Accidents between vehicles on crossing paths 

Pink 

– Accidents with vulnerable road users 

Blue 

For cases which have a GDV‐code that has not been categorised in this document, the analyst should 

categorize it by going to the accident details and try to determine in which group the case belongs 

In those cases where the GDV‐code has been categorised twice the analyst has to determine in which 

group the accident belongs to 

Taken from Glossary of Data Variables for Fatal and Accident causation databases (Reed and Morris, 2008) 

1

GDV Type 1: Driving Accident 

GDV Definition: A driving accident occurred when the driver looses control over 

his vehicle because he chose the wrong speed according to the run of the road, 

the road profile, the road gradient or because he realised the run of the road or a 

change in profile too late. 

Driving accidents are not always single vehicle accidents where the vehicle leaves 

the road. A driving accident can also lead to a collision with other road users. 


2

Type 1 (Driving accident): Search for accident subtypes Page 1 of 2 

Driving accident without influences by road width or lateral gradient 

Type 10 

In a curve 

Type 11 

In a curve with 

turning priority 

Type 12 

Turning in or off 

to another road 

Type 13 

At a swaying road 

Type 14 

On a straight 

road 

Taken from Glossary of Data Variables for Fatal and Accident causation databases (Reed and Morris, 2008)

Type 1 (Driving accident): Search for accident subtypes Page 2 of 2 

Driving accident with influence of… 

Type 15 

…gradient 

Type 16 

…traffic island 

Type 17 

…road narrowing 

Type 18 

… uneven road 

Type 19 

… other driving 


Other driving accidents 199 


GDV Type 2: Turning off Accident 

GDV Definition: A turning accident occurred when there was a conflict between a 

turning road user and a road user coming from the same direction or the opposite 

direction (pedestrians included!). This applies at crossings, junctions of roads and 

farm tracks as well as access to properties or parking lots. 


5

Type 2 (Turning off accident): Search for accident subtypes Page 1 of 2 

Type 20 

Conflict between a vehicle turning off to the 

left and following traffic 

Type 21 


left and oncoming traffic 

Type 22 


left and a vehicle from a special path/track or 

a pedestrian going to the same or opposite 

direction 

Type 23 


right and following traffic 

Type 24 


right and a veh. from a special path/track or a 

pedestrian moving in to the same or opposite 

direction 


Type 2 (Turning off accident): Search for accident subtypes Page 2 of 2 

Type 25 

Conflict between two turning off vehicles, 

moving along side in the same direction. 

Type 26 

Conflict between a turning off vehicle and a 

vehicle without priority, waiting at the headed 

road of the turning veh. 

Type 27 

Conflict between a turning off veh. from a 

priority rd and another road user at a traffic 

junct. with a turning priority road. 

Type 28 

Conflict between a turning off veh. and 

another rd user coming from the same or the 

opposite direction when the turning traffic is 

regul. by traffic lights. 

Type 29 

Other turning off accidents 

Other turning off accidents 299 


GDV Type 3: Turning in / crossing accident 

GDV Definition: A turning in / crossing accident occurred due to a conflict between 

a turning in or crossing road user without priority and a vehicle with priority. This 

applies at crossings, junctions of roads and farm tracks as well as access to 

properties or parking lots. 


8

Type 3 (Turning in / crossing accident): Search for accident subtypes 

Page 1 of 2 

Type 30 

Conflict between a non priority vehicle and a 

priority vehicle coming from the left, which is 

not overtaking. 

Type 31 


priority vehicle coming from the left, which is 

overtaking. 

Type 32 


priority vehicle coming from the right, which 

is not overtaking. 

Type 33 


priority vehicle coming from the right, which 

is overtaking. 

Type 34 


bicyclist with priority coming from a bicycle 

path. 


Type 3 (Turning in / crossing accident): Search for accident subtypes 

Page 2 of 2 

Type 35 


priority vehilce on a turning priority road. 

Type 36 

Conflict between vehicle and a railway vehicle 

at a level crossing. (Unless it is a turning off 

accident) 

Type 37 

Conflict between a vehicle and a bicyclist 

coming from a parallel bicycle path who is 

turning in to or crossing the road. 

Type 39 

Other turning in / crossing accidents 

Other turning in / crossing accidents 399 


GDV Type 4: Pedestrian Accident 

GDV Definition: A pedestrian accident has occured due to a conflict between a 

pedestrian crossing the road and a vehicle unless the vehicle was turning off. This 

is independent of whether the accident occurred at a place without special 

pedestrian crossing facilities or at a zebra crossing or similar. 


11

Type 4 (Pedestrian accident): Search for accident subtypes 

No junction Before a junction Behind a junction 

(types 40-42) (types 43-45) (types 46, 48, 49) 

Type 40 

Conflict between a pedestrian coming from 

the left and a vehicle. 

(Unless type 41) 

Type 41 


the left and a vehicle which had an 

obstructed line of sight by parking vehicle, 

tree, fence …. 

Type 42 


the right and a vehicle. 





Type 43 



(Unless type 44) 

Type 44 


the left and a vehicle which had an 

obstructed line of sight by parking vehicle, 

tree, fence …. 

Type 45 


the right and a vehicle. 





Type 46 



Type 47 

c 


the right and a vehicle 

Type 48 

Conflict between a pedestrian and a vehicle 

following a turning priority road. 

Type 49 

Conflict between a vehicle and a pedestrian 

crossing a junction diagonally, or getting 

on/off a tram. 

As well as other pedestrian accidents. 


GDV Type 5: Accident with parking traffic 

GDV Definition: An accident with standing traffic occurred due to a conflict 

between a vehicle from moving traffic and a vehicle which is parking, has stopped 

or is manoeuvring to park or stop. This is independent of whether 

stopping/parking was permitted or not. 


15

Type 5 (Accident with parking vehicles): Search for accident subtypes 

Page 1 of 2 

Type 50 

Conflict between a vehicle and a parking 

vehicle in front. 

Type 51 

Conflict between a vehicle swinging out to 

avoid a parking vehicle and a following 

vehicle. 

Type 52 


avoid a parking vehicle and an oncoming 

vehicle. 

Type 53 


avoid a parking vehicle and a pedestrian. 

Type 54 

Conflict between a vehicle which is stopping 

to park or entering a parking space and a 

vehicle of the moving traffic. 


Type 5 (Accident with parking vehicles): Search for accident subtypes 

Page 2 of 2 

Type 55 

Conflict between a vehicle driving away or 

leaving a lateral parking space and a vehicle 

of the moving traffic. 

Type 56 

Conflict between vehicle leaving a transverse 

parking space forewards and a vehicle of the 

moving traffic. 

Type 57 

Conflict between vehicle leaving a transverse 

parking space backwards and a vehicle of the 

moving traffic. 

Type 58 

Conflict because of opening a vehicle door, 

getting into /out of the vehicle or loading. 

Type 59 

Conflict between a turning vehicle and a 

parking vehicle which is located at the headed 

path – as well as other accidents with parking 

vehicles. 


Type 6: Accident in lateral traffic 

Definition: The accident in lateral traffic occurred due to a conflict between road 

users moving in the same or in the opposite direction. This applies unless the 

conflict is the result of a conflict corresponding to another accident type. 


18

Type 6 (Accident in lateral traffic): Search for accident subtypes 

Page 1 of 2 

Type 60 

Conflict between a vehicle and another vehicle 

driving in front on the same lane. 

Type 61 

Conflict between a vehicle which is braking, 

standing or going slow due to a traffic jam 

and a following vehicle. 

Type 62 

Conflict between a veh. wh. is braking, 

standing or going slow due to traffic or non 

priority and a following vehicle. 

Type 63 

Conflict between a vehicle which is changing 

lanes to the left and a following vehicle on the 

lane alongside. 

Type 64 

Conflict between a vehicle which is changing 

lanes to the right and a following vehicle on 

the lane alongside. 


Type 6 (Accident in lateral traffic): Search for accident subtypes 

Page 2 of 2 

Type 65 

Conflict between two vehicles, side by side, 

going in the same direction. 

Type 66 

Conflict between an overtaking vehicle and a 

vehicle from oncoming traffic, a pedestrian or 

a parking vehicle. 

Type 67 

Conflict between vehicle which is not 

overtaking and a pedestrian on the same 

lane. 

Type 68 

Conflict between two head-on encountering 

vehicles. 

Type 69 

Other accidents in lateral traffic. 

Other accidents in lateral traffic 699 


Other accident type 

Definition: Other accidents are accidents, that cannot be assigned to the accident 

types 1-6. Examples: Turning around, backing up, accidents between two parking 

vehicles, objects or animals on the road, sudden vehicle defects. 


21

Type 7 (Other accidents): Search for accident subtypes Page 1 of 2 

Type 70 

Accident with two parking vehicles. 

Type 71 

Accident while backing up or rolling back. 

Unless manoeuvring to park 

Type 72 

Accident due to a u-turn. 

Type 73 

Accident due to a not fixed object. 

Type 74 

Accident due to a broken down vehicle. 


Type 7 (Other accident): Search for accident subtypes Page 2 of 2 

Type 75 

Accident due to an animal on the road. 

Type 76 

Accident due to a sudden physical disability of 

a road user. 

Type 77 

Accident due to a sudden technical defect on 

the vehicle. 

Type 79 

All other accidents 

Other accidents 799 


Pictograph explanation 

Accelerating vehicle 

Decelerating (braking) vehicle 

Pedestrian 

Standing vehicle (due to traffic) 

v 

Parking vehicle 

Backing-up vehicle 

Skidding / swerving vehicle 

W = non priority vehicle / vehicle having to wait 

P = Pedestrian 

B = Bicyclist 

T = Train / Tram 


APPENDIX C, List of ACASS Codes 

First 

no. 

(1) Human factors 

Second 

no. 

(Category) 

(1) 

Informatio 

n access 

Code if the 

participant did not 

have access to 

relevant 

information at the 

emergence of the 

accident. An 

available piece of 

information 

cannot be 

perceived if it was 

covered / hidden 

by objects inside 

or outside the 

vehicle of if it 

could not be 

registered due to 

physical 

conditions or 

disease. 

(2) 

Observatio 

n 

(3) 

Recognitio 

n 


Group 1: Human factors (First number of code= 1) 

01 

02 

03 

04 

01 

02 

03 

04 

01 

02 

Third number 

(Criteria) 

Information not 

perceivable due to 

disease or physical 

condition 


hidden/covered by 

objects outside the 

vehicle 

Applies for objects which 

are not connected with the 

vehicle 


hidden/covered by 

objects inside the 

vehicle 

This also includes trailers 

and external objects fixed 

to the vehicle 

Informationmasking 

By atmospheric conditions 

or lack of contrast 

Distraction from 

inside the vehicle 

Distraction from 

traffic environment 

Internal distraction 

(thoughts / 

emotions) 

Activation too low 

Attention hindered/reduced 

due to physiological 

conditions. Resulting in a 

reduction of information 

admission 

Wrong identification 

due to excessive 

demands 

„Information overload“ 

Wrong focus of 

attention 

When observing the traffic 

situation the attention went 

towards the relevant 

objects, but the directly 

relevant information for the 

correct action was 

missed/overlooked 

(collision partner). 

Fourth number 

(Indicator) 

(0) Not. Specified (8) Other (9) Multiple 

(1) Seeing problem. \ / wrong or not corrected 

(2) Hearing problem. / \ problems with eyes or ears 

(0) N.s. (8) Other (9) Multiple 

(1) Buildings 

(2) Plants 

(3) Parking vehicles 

(4) Standing or moving vehicles 


(1) Passengers 

(2) vehicle-load 

(3) steamed-up / frosted windows 

(4) Retrofit devices (mobile GPS-navigation) 

(5) bodywork pillars and other components 


(1) Darkness 

(2) Heavy rain 

(3) Fog 

(4) Dazzle (Sun, other vehicles) 

(5) superimposition of relevant information 

(other light sources, similarity of colour) 

(6) sound overlapped by noise 


(1) Operation of devices 

(2) by passengers 

(3) On the phone / Music 

(4) Animals 


(1) Posters, showcases etc. 

(2) People 


(1) Irritation, anger 

(2) Sadness, worries 

(3) Hurry, stress 

(4) Exhilaration, euphoria 


(1) physical stress, fatigue 

(2) Alcohol 

(3) Drugs 

(4) Disease / Medicine 

(5) Blackout (Heart attack, seizure) 


(1) Complex information (stimulus satiation) 

(2) Complexity (not the amount of information 

t but the arrangement ) 


(1) Focus on other road user 

(observing other road users but 

overlooking the relevant road user) 

(2) Focus on traffic sign (Traffic lights, signs) 

(3) Wrong strategy of observation 

(omission of reorientation or control look) 

1


First 

no. 

(1) Human factors 

Second no. 

(Category) 

(4) 


evaluation 

The participant 

has observed and 

recognized the 

relevant 

information, but 

has made an 

evaluation or 

estimation error. 

(5) 

Planning 

The information 

was correctly 

taken in and 

evaluated but the 

conclusions drawn 

from this for an 

action to cope with 

the situation were 

wrong. This does 

not relate to reflex 

actions. 

01 

02 

03 

01 

02 

Third number 

(Criteria) 

Wrong 

expectation 

concerning the 

accident place or 

the behaviour of 

other road users 

due to wrong 

assumptions 

Misjudgement of 

speed/distance of 

other road users 

Misjudgement 

concerning own 

vehicle 

(dynamic condition or 

reaction of own vehicle 

in the critical situation) 

Decision error 

Having enough time to 

choose an action 

strategy, the participant 

has opted for the wrong 

action alternative. 

Intentional breach 

of rules 

Only intentionally 

conducted breach of 

rules; not applicable if 

lack of information or 

driving under influence 

of alcohol. 

(6) 

Selection 01 Reaction error 

Fourth number 



(1) Communication error (between road users) 

(2) lack of knowledge of accident place 

(3) Wrong confidence due to habits / 

experiences (Frequently experiencing a 

traffic situation leads to a wrong evaluation 

of information. “never before a car came out 

of this road“) 


(1) Misjudgement of speed of other road user 

(2) Misjudgement of distance of other road user 


(1) underestimation of own speed 

(2) Vehicle behaviour (dynamic, stability) 

(3) Misjudgement of braking power or 

accelerating power. 

(4) Misinterpretation of driving assist. systems 

(displays, lamps, warning signals) 


(1) Wrong manoeuvre planned (e.g. 

Evasive manoeuvre instead of braking) 

(2) Wrong assumption concerning the 

Development of the situation (The movement 

of other road users was assumed wrongly) 


(1) Neglecting right of way 

(2) Speeding 

(3) wrong overtaking 

(4) wrong turning off 

(5) To tailgate so. 


(1) braked too weak 

(2) braked too late 

(3) braked too strong 

(4) steered too weak / too late / omitted 

(5) Overreaction of steering 

(6) omitted reaction 

(7) 

Operation 

01 

Mix-up and 

operation error 


(1) Pedals 

(2) gear shift 

(3) controls 

2


Group 2: Technical factors from the vehicle (First number of code= 2) 

First 

no. 

Second no. 

(Category) 

Third number 

(Criteria) 

Fourth number 


(2) Technical factors from the vehicle 

(1) 

technical defect 

(2) 

Illegal technical 

alteration 

(3) 

Human - machine 

interface 

(00) Not specified 

(01) Brakes 

(02) Steering 

(03) Tires / wheels (tread depth, damages, 

puncture) 

(04) Suspension 

(05) car body 

(06) engine 

(07) drive train 

(08) Light (external) 

(09) vehicle electrics 

(10) vehicle electronics – intervening 

driver assistance systems 

(11) vehicle electronics – Info-Systems 

(LDW, parking sensors, IR-Cam…) 

(12) control elements 

(13) interior illumination 

(14) tie down of load 

(15) Other (16) Multiple 


(01) Brakes 

(02) Steering 

(03) Tires / wheels 

(04) Suspension 

(05) car body 

(06) engine 

(07) drive train 

(08) Light (external) 

(09) vehicle electrics 

(10) vehicle electronics – intervening 

driver assistance systems 

(11) vehicle electronics – Info-Systems 

(LDW, parking sensors, IR-Cam…) 

(12) control elements 

(13) interior illumination 

(14) Tie down of load / overload 



(01) Reach (Access to control elements) 

(02) Inappropriate illumination 

(03) Complexity, intuitive usability 

(04) Noise (e.g. annoyance be vehicle 

noise) 

(05) line of sight obstruction e.g. to 

Displays or control elements 

(06) Insufficient / wrong information 

(e.g. wrong indication from 

Navigation system or speedometer) 


(0) 

(0) 

(0) 

3


Group 3: Factors from environment and infrastructure (First number of 

code= 3) 

First 

no. 

Second no. 

(Category) 

Third number 

(Criteria) 

Fourth number 


(3) Factors from environment and infrastructure 

(1) 

Condition/ 

maintenance 

(of infrastructure) 

(2) 

Road design 

(3) 

Environmental/Weat 

her factors 

(4) 

Other external 

influencing factors 

(00) not specified 

(01) condition of roadway (pot-holes, 

repair patches, lane grooves etc.) 

(02) contamination of road surface 

(03) condition of road marking 

(04) condition of roadway shoulder 



(01) design of crossing / 

Traffic environment 

(02) horizontal/vertical alignment 

(drag curve, too steep roads) 

(03) Temporary constructional changes 

(04) design of road surface 

(05) inappropriate traffic guidance 

(missing signs, deficient traffic lights) 

(06) Optical guidance (suggestion of other 

Run of road) 



(01) physical influence from storm 

(crosswind, lightning) 

(02) condition of road surface due to 

rain 


ice 


snow 



(01) animals 

(02) Intervention through third parties 

e.g. hooliganism/disordering 

(03) Falling, flying objects 

(04) Foreign objects on the roadway 


(0) 

(0) 

(0) 

(0) 

4

In-depth accident causation database and analysis report - ERSO

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