Deliverable 3 Report on detailed data and ... - QUANTIS Project

<strong>Deliverable</strong> 3
<strong>Report</strong> on detailed data and service quality
Munich, May 2010

QUANTIS –
Quality Assessment and Assurance methodology for Traffic data and
Information Services
Contract n.: TREN-07-MD-S07.80121
Duration: 01.07.2008 – 30.06.2010
European Commission – DG TREN
Partners:
AustriaTech – Federal Agency for Technological Measures, Austria
Pöyry Infra Traffic GmbH, Germany
Oberste Baubehörde – Bavarian Road Administration, Germany
VTT – Technical Research Centre of Finland, Finland
WSP Group, United Kingdom
Website: www.quantis-project.eu
<strong>Deliverable</strong> n.: 3
Title:
<strong>Report</strong> on detailed data and service quality
Authors: Astrid Kellermann, Claudia Gessner, Thomas Scheider, Risto Öörni, Doug
Newton, Peter Pollesch, Ulrich Haspel
Version: 1.5.
Date: May 2010
Status: final
Distribution: QUANTIS partner
This document should be referenced as:
Kellermann, A.; Gessner, C.; Scheider, T.; Öörni, R.; Newton, D., Pollesch, P., Haspel, U.
(2009): <strong>Deliverable</strong> 3 of QUANTIS – <strong>Report</strong> on detailed data and service quality. Munich,
Germany.
Version Date Released by Description
1.0 12.08.2009 PIT Initial Set-Up
1.1 28.09.2009 PIT Further elaboration
1.2 21.10.2009 PIT Further elaboration, inclusion of contributions
1.3 21.12.2009 PIT Further elaboration, inclusion of contributions
1.4 20.01.2010 PIT Further elaboration, inclusion of contributions
1.5 11.05.2010 PIT Final
Project co-funded by the
European Commission –
Directorate-General for
Energy and Transport

Executive summary
<strong>Deliverable</strong> D3 “Detailed data and service quality” is a joint effort of the project
partners Pöyry Infra Traffic, AustriaTech, VTT Technical Research Centre of Finland and
WSP. It is the final outcome of the work package 400 within the EC co-financed project
QUANTIS (Quality Assessment and Assurance Methodology for Traffic Data and
Information Services).
The aim of the QUANTIS project is to investigate the relationship between ITS service
quality and benefits/costs, to determine the optimum service quality in four European
service cases, to identify levels of data quality providing optimal service quality and to
give a recommendation for European guidelines for quality assurance of traffic data.
For the determination of detailed data and service quality, a quality assessment
scheme has been developed, including the detailed definition of quality levels. The
deliverable describes the methodology for the quality assessment and the detailed
definition of the general quality objects and the quantified quality parameters which
serve for the assessment of the given quality level for operational ITS.
This includes the following steps:
- service specification along the data processing chain by means of a common
matrix (Task 2 and 3 of WP 400)
- definition of quality parameters for selected services and quality objects (Task 4
of WP 400)
- application of the methodology for case studies in co-operation with WP 600 and
evaluation of benefits and costs for defined quality levels (Task 5 of WP 400)
Finally, within this WP, the collection of empiric and operational data will be launched,
as a basis of the evaluation (WP 600). A description will be provided about the data
collected at different steps of the data processing chain for each of the QUANTIS case
studies.

Table of Content
Executive summary..........................................................................................I
Table of Content............................................................................................. 1
List of Figures ................................................................................................ 2
List of Tables.................................................................................................. 3
List of Abbreviations........................................................................................ 4
1. Introduction ............................................................................................... 5
2. The QUANTIS quality assessment scheme – requirements, approach and application
7
2.1 Introduction ...........................................................................................................7
2.1.1 Requirements definition................................................................................................7
2.1.2 Methodological Approach ..............................................................................................8
3. Steps of the QUANTIS assessment scheme.....................................................11
3.1 Step 1: Specification of the service to be assessed....................................................11
3.1.1 Data collection..........................................................................................................12
3.1.2 Related information ...................................................................................................13
3.1.3 Data congregation.....................................................................................................13
3.1.4 Customer oriented services .........................................................................................13
3.1.5 Carriers ...................................................................................................................14
3.2 Step 2: Specification of quality objects and quality parameters...................................14
3.2.1 List of quality objects and parameters...........................................................................14
3.2.2 Definition of quality objects and parameters for selected services ......................................16
3.3 Step 3: Definition of quality level thresholds.............................................................17
3.4 Step 4: Weighting of contents, quality objects and quality parameters and definition of
quality levels .............................................................................................................17
3.5 Step 5: Assessment of a customer oriented service by technical benchmarking ............21
3.6 Step 6: Determination of total service quality...........................................................21
3.7 Step 7: Definition of target values (optimal service quality)........................................23
3.8 Step 8: Cost estimation .........................................................................................24
3.9 Step 9: Ranking of improvement actions..................................................................24
3.10 Visualisation of quality assessment results by means of spider diagrams....................25
4. Acquisition of empiric data from case studies..................................................26
4.1 Bavaria: Assessment of dynamic traffic situation.......................................................26
4.1.1 Description of the Bavarian case study..........................................................................26
4.1.2 Overview about data collection for quality assessment of dynamic traffic situation information
under www.bayerninfo.de ...................................................................................................28
4.1.3 Real traffic data collection...........................................................................................28
4.2 Austria: Assessment of RDS-TMC............................................................................30
4.2.1 Description of the Austrian case study...........................................................................30
4.2.2 Overview about data collection for quality assessment of TMCpluc service in Autria ..............31
4.3 Finland: Assessment of weather information services ................................................33
4.3.1 Description of the Finnish case study ............................................................................33
4.3.2 Data collection in the Finnish case study........................................................................33
References....................................................................................................34
1. Annex: Matrix for the description of the data processing chain...........................35
2. Annex: Description of the data processing chain by means of the data processing
matrix for the QUANTIS case studies.................................................................35
3. Annex: Detailed list and definition of quality objects and quality parameters for
selected ITS services including parameter quantification for quality levels ...............35
4. Annex: End-user survey for the determination of weighting values for quality
objects and contents ......................................................................................35
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List of Figures
Fig. 1: Data Processing Chain for ITS.................................................................. 5
Fig. 2: Methodological Approach for the QUANTIS quality assessment scheme........... 8
Fig. 3: Quality Function Deployment/House of Quality by Akao..............................10
Fig. 4: Results of user questionnaires.................................................................20
Fig. 5: Example of a spider diagram, aiming to visualize obtained quality ................25
Fig. 6: Dynamic traffic situation service on the internet portal of the Bavarian traffic
information agency....................................................................................27
Fig. 7: Data collection loop on the motorway ring road A99 and the motorway A8 to
Salzburg..................................................................................................29
Fig. 8: Sample collection along the data processing chain of the Bavarian case study.30
Fig. 9: Data sources and data exchange links for the Austrian TMC Plus service........31
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List of Tables
Tab. 1: Requirements defined for the QUANTIS quality assessment scheme.............. 7
Tab. 2: Matrix of the data processing chain.........................................................12
Tab. 3: Relevance of quality objects along the data processing chain ......................15
Tab. 4: Relevance of quality objects for selected core-services (survey results) ........19
Tab. 5: Relevance of core services for dynamic traffic situation (survey results)........19
Tab. 6: Calculation of total service quality ..........................................................22
Tab. 7: Ranking of actions according to quality assessment and set targets .............23
Tab. 8: Data collection for the quality assessment of bayerninfo.de ........................28
Tab. 9: Data collection for TMCplus, Austria........................................................32
Tab. 10: Data collection for weather information, Finland......................................33
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List of Abbreviations
EC European Commission
ITS Intelligent Transport Systems
NQAV Non-Quality Assessment Value
PC Personal Computer
QAV Quality Assessment Value
QFD Quality Function Deployment
RDS/TMC Radio Data System/Traffic Message Channel
SQL Service Quality Level
SQF Service Q
TSQ Total Service Quality
TWF Total Weighting Factor
WFCS Weighting Factor Core Service
WFQO Weighting Factor Quality Object
WP Work Package
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1. Introduction
A basic prerequisite for the effective implementation of robust cross border traffic
management is the quality and quantity of data/information. Any quality imbalance
between parties undermines the basis for joint operations. Significant shortages have
been identified in the EasyWay project (www.easyway-its.eu) and its preceding Euro-
Regional Projects with regard to methodologies required to evaluate and optimise data
and service levels in terms of quality and costs, preventing cross border data exchange
and service provision.
It is the objective of QUANTIS to analyse the relationship between data and service
quality, user benefits and to recommend common assessment and quality assurance
methods of European traffic services in order to support policy objectives.
This deliverable summarizes the results of WP 400 “Determining detailed data and
service quality”. It is based on the work carried out in WP200 “Key European ITS
Services and data quality” and WP300 “Determining service benefits and costs” and it
details the developed quality assessment scheme for ITS data and services, including
the detailed quality level definition for defined European case studies.
When assessing service quality, it is essential to consider the entire data processing
chain.
The following figure shows the data processing chain for ITS in a generalized way.
Fig. 1: Data Processing Chain for ITS
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Data, e. g. traffic data, event data or weather data, are collected by different data
sources and submitted to a content platform. There, data are checked, merged,
aggregated and transformed into different contents, e. g. into “traffic situation”,
current and predicted travel times or events.
The output of the next step of the data processing chain, the service platform, is a
customer oriented service, which might integrate different contents. For example when
providing traffic information to the end-user, information about the traffic conditions
often are combined with additional relevant information, such as construction site
information, information about public events or weather data. Furthermore, the
contents must be provided in different ways according to the carriers which shall
submit information to the end-user. Thus input data for RDS/TMC information usually
differs from input data for information via internet portals.
Finally the information is provided to the end-user via specific carriers, e. g. via TMCradio,
PC at home or mobile devices.
All the steps of the data processing chain have an influence on the quality of the
service, e. g. a certain data quality is the prerequisite for a good service quality,
however quality might increase or decrease along the chain. It can be enhanced, e. g.
if insufficient data is supplemented by additional information or data from other
sources, such that merged contents of high quality are obtained. But quality can also
be reduced, e. g. if data sources provide high quality data but the algorithms at the
content platform provide insufficient output with errors. Quality evaluation is always
dedicated to a specific step of the data processing chain.
The methodology developed in QUANTIS can in general be applied at all steps of the
data processing chain.
The scope of QUANTIS has been defined such that quality objects and parameters are
defined in detail at service platform level, according to the objectives of the case
studies involved. Definitions for other interfaces, e.g. at data processing or end-user
interface level, have not been defined and would require modifications in the definition
of quality objects and parameters.
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2. The QUANTIS quality assessment scheme –
requirements, approach and application
2.1 Introduction
2.1.1 Requirements definition
On the basis of the work in WP 200, 14 basic requirements can be summarized for the
quality assessment scheme in QUANTIS (see QUANTIS <strong>Deliverable</strong> 1: “Definition of key
European ITS services and data types”): six (R1-R6) concerning its applicability and
eight (R7-R14) concerning the procedure.
Applicability
R1 The assessment scheme can be applied to every key ITS service which specifies
transport telematic services.
R2 The assessment scheme can be applied at all processing steps of the traffic
information.
R3 The comparison of quality at different processing steps allows for the optimization
and evaluation of quality along the processing chain.
R4 The assessment scheme must allow the comparison of the traffic information for
different sources, different providers and different distribution channels.
R5 The quality of the service has to be definable for both, current events as well as
prognosis.
R6 Quality must be definable independently from the presentation format.
Procedure
R7 The assessment needs to cover the following quality objects:
Completeness
Availability
Veracity
Precision
Timeliness (appearance and cancellation of events)
Consistency
Relevance
R8 Each quality object needs to be broken down to measurable parameters.
R9 When evaluating a service with the help of the assessment scheme it is necessary
to apply data collected under real traffic conditions.
R10 The quality should be indicated with one quality assessment value. The value is
determined via weighted quality objects and parameters.
R11 The weighting of objects and parameters needs to be equivalent for all partners
who exchange information.
R12 The quality assessment value must reflect the effects of the service.
R13 The quality claimed by the provider must be considered. This includes for example
the planned coverage (physical, geographical, data types) and availability period.
R14 Consistency parameters need to reflect the synchrony of output channels and the
logical consistency.
Tab. 1: Requirements defined for the QUANTIS quality assessment scheme
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2.1.2 Methodological Approach
The following picture (Fig. 2) shows the methodological approach of the QUANTIS
assessment scheme which comprises nine steps.
2
Quality specification:
Quality objects
Quality parameters
1
Service
Specification
(data processing chain)
3
4
5
Weighting of contents,
objects and parameters
Definition of quality levels
Technical
Benchmarking
In %
Level 0 (Insufficient
Quality) – Level 4 (High
Quality)
6
7
Total service quality
Target values
8
9
Estimation of cost effort
for improvements
Ranking of actions
Fig. 2: Methodological Approach for the QUANTIS quality assessment scheme
1) Specification of the service by a description of the entire data processing chain
with the help of a common matrix – this enables transparency about the data,
contents, core services and carriers affected and the investigation of
redundancies and/or possible gaps
2) Specification of service quality by means of general quality objects and
quantified, measurable quality parameters – a detailed elaboration is carried out
in QUANTIS, distinguishing different core services.
3) Weighting of contents, objects and parameters in %, according to the given
importance of the service to be assessed – as the relevance of contents and also
parameters differs for the different services, it is necessary to carry out a
corresponding weighting, considering in particular the end-users perspective
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4) Based on measurable parameters, five quality levels are defined, from
insufficient (level 0) to high quality (level 4). These levels later serve to
determine the achieved service quality in terms of quality objects for given
operational services.
5) Assessment of the service by technical benchmarking - collected data from real
traffic environment and operation is compared to the thresholds of the quality
levels
6) Determination of total service quality – the total service quality is the final result
of the technical benchmarking. The calculation also integrates the defined
weighting values for contents and quality objects. (from the logic order steps 5
and 6 are carried out after step 4)
7) Definition of target values under consideration of providers´ objectives - this
serves to compare targets and objectives with the actually reached quality and
to identify quality gaps (step 7 can be carried out in parallel to step 4 as it does
not rely on steps 5 and 6)
8) Estimation of costs for improvements, where gaps and inferiorities are identified
between given quality and targeted quality
9) Ranking of actions for improvements – this involves the weighting factors, the
identified gaps and the estimated improvement costs.
Steps 2, 3, 4 and 7 (yellow boxes of the diagram) include the definition of objects,
parameters, weighting factors and target values. These steps will be carried out before
the evaluation of the service.
Steps 1, 5, 6, 8 and 9 (brown boxes of the diagram) are dedicated to the service
specification, evaluation and the definition of improvement actions.
The methodological approach shows some similarities with the Quality Function
Deployment tool by Prof. Yoyi Akao, better known as “House of Quality”. By means of
different matrices the QFD helps
to define relationships between customer requirements and technical features
to assess competitive product features and
to define targets and to rank actions
The following picture illustrates the approach according to Prof. Akao.
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Fig. 3: Quality Function Deployment/House of Quality by Akao
The QFD tool has influenced the development of QUANTIS assessment methodology,
but differs in the scope of application. The QFD is usually applied in the design stage of
a product or service, whereas QUANTIS’ focus is to assess already existing services.
Further, competitive benchmarking and also the strategic positioning via product
features, is out of scope of the QUANTIS assessment.
Features, which directly relate to the end-user interface and not to the service output
interface, are not covered in QUANTIS’ assessment.
The basic differences between the two tools are:
due to the fact that the QUANTIS methodology shall be applied at the different
steps of the data processing chain, but in particular to the service level, more
emphasis is given to operational parameters
weighting of parameters according to use case (performance based contract,
automated selection of data sources) is applied in QUANTIS instead of feature
rankings
possibility to represent the evaluation results in a single factor according to the
defined requirements and
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visualisation of reached quality levels for the parameters/core services is carried
out by means of spider diagrams
In general it can be summarized, that the QUANTIS approach is tailored to suite the
ITS service requirements and has been developed according to the given tasks and
objectives definition of the QUANTIS project.
3. Steps of the QUANTIS assessment scheme
The QUANTIS quality assessment scheme is composed of nine steps in total. The
following description explains the full procedure aimed at determining the total service
quality as well as suitable actions of improvement.
3.1 Step 1: Specification of the service to be assessed
The specification of the data processing chain is carried out by a matrix, consisting of
five sub-matrices.
It describes
(1) The interrelations between data sources and data types
(2) Data types and relevant supplementary information, e. g. location, time and
others
(3) The correlation of data types to contents (i. e. core services)
(4) The integration of different contents (i. e. core services) to customer oriented
services
(5) The carriers for the submission of customer oriented services to the enduser.
The matrix has been developed in a comprehensive way for all Traffic Information
Services (TIS) and all relevant and possible interrelations/options along the data
processing chain are marked by grey fields.
When applying the matrix for a given customer oriented service type and for the
QUANTIS case studies, the actual combinations realised are marked in the grey fields
with crosses.
The specification of a customer oriented service by means of the matrix shall help to
clarify the given data and information and also to identify possible supplements and
improvements.
The matrix is provided in Annex 1.
Examples of the matrix filled in for the QUANTIS case studies are provided in Annex 2.
The following picture shows the matrix in an overview.
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Rela ted
Event informa-
Time Location
ti on
Re la ted Information
Location (point)
Location (l ink)
Location (POI)
Parking pl aces
P+R pla ces
Location (PT stations)
PT lines
Location (area)
Extension
lane(s) affected
time stamp
time horizon
durati on
expected impact on traffic
recommendations
warnings
Data Collection
Data Sources
traffic detectors
historic traffi c data bases
map provider
static speed limit DB
weather detectors
commerci al weather services
pol ice
road operator/MCC/Tunnel
road works managem ent
Calendar
Cameras
PT operators
P+R operator
parking operator
UTC operator
traffic reporting driver
traffic reporting driv er GPS
broadcasters
Automatic num ber plate recognition
FCD
Telecom operator
Data Congregation
Core Services
Construction sites
accidents/warnings
planned events/calendar
Traffic c onditions real-time
Traffic conditions predicted
Travel T ime
Weather Information current
Weather Information pred.
Speed limit information
Co-modal information
Categories
D ata T yp es
2 – Related Information
T raf fic data
Traffi c c ontrol data
Ev en ts
traffic volume cars
traffic volume HGV
speed cars
speed HGV
traffic images
Historic traffic data (volume,
speed, travel time)
travel time
traffic control data (speed lim it
data -static and dynamic, signal
control, no passing)
re-routing
ramp-metering
construction sites, road works
Accidents/Incidents
publi c events, holiday
Points of Intrest
road surface (temperature, friction) Weather
atmospheric data (temperature,
humidi ty, precipitation, vi sibility,
wind)
weather data predi cti ve (surface,
atmospheric, wind)
1 – Data Collection
Data Sources Data Types
3 – Data Congregation
co-modal data
timetables
disturbances, delays,
cancellations expected
disturbances, delays,
cancellations unexpected
additional PT supply
P +R faci lities - s tat ic
P+R occupancy real-time
Parki ng fac ilities - static
Parking occupancy real -time
Carriers
RDS / TMC
mobi le devi ces
internet portal
Customer Oriented S ervices
D-Bavaria A FIN UK
Case Studies
routing
dynam ic traffic situation
real-time
dynamic traffic situation
pre-dicted
Traff ic si tuation T MC+
weather services
dynamic journey time
information
4 – Customer Oriented Services
event m anagement
5 – Carriers
Tab. 2: Matrix of the data processing chain
3.1.1 Data collection
The first step in the data processing chain is data collection by different data sources in
order to generate the services’ contents.
In QUANTIS a list of relevant data types has been developed in WP 200 (<strong>Deliverable</strong> 1)
which groups data types according to the following data categories:
Traffic data
Traffic control data
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Weather data
Event data
Co-modal data
A data type can be provided by different data sources (e. g. an incident can be
detected by traffic detectors, cameras, the police or drivers). Possible data sources for
different data types are marked by grey fields.
3.1.2 Related information
Each data type is accompanied by related indispensable information, e. g. in terms of
time, location and further relevant details, depending on the situation. This related
information has a significant influence on both data and service quality. For each data
type, e. g. travel times, at least the corresponding valid location and time is needed.
Other data types require additional information, e. g. recommendations or warnings.
3.1.3 Data congregation
The third step in the data processing chain is the content platform where data is
merged in an event related way according to specific contents. This means that data
obtained from different sources and related to the same event must be checked in
terms of plausibility, cross-verified and merged in order to obtain a higher level of
information quality, e. g. in terms of accuracy, timeliness and/or completeness. For
example some sources might provide a better data quality in terms of local accuracy,
others in terms of timeliness or in terms of level of detail.
Within QUANTIS it has been decided to group the contents generated on the content
platform according to the TIS- core services defined within the EasyWay TIS Expert
Group, i.e.
Event Information (construction sites, accidents and warnings and planned events)
Traffic Conditions (real-time and predicted)
Travel Time
Weather Information (real-time and predicted)
Speed Limit Information
Co-modal Services
The specification of these core services is available in detail in <strong>Deliverable</strong> 2 “Core
European ITS Services and Actions” from September 2008, of EASYWAY European
Study 6 “The EASYWAY ITS Roadmap”. The final specification will be available in the
<strong>Deliverable</strong> 4 describing the detailed deployment guidelines for the core services which
will be published in December 2009.
3.1.4 Customer oriented services
The fourth step of the data processing chain is the provision of customer oriented
services. Here it normally is necessary to combine two or more of the core services.
Customer oriented services depend on the carrier by which they are provided.
Examples for customer oriented services are the QUANTIS case-studies.
For example traffic situation information provided by the Bavarian internet portal
www.bayerninfo.de consists of the traffic situation itself (shown on a map with the help
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of different colours) as well as construction site information, weather information and
information about public events, such as fairs. In addition to the map based
information, details are provided in a list of messages related to the pictograms on the
map.
3.1.5 Carriers
The fifth step of the data processing chain is the provision of information to the enduser
via specific carriers, e. g. internet portal, mobile devices or RDS-TMC. The
carrier-specific output also influences the quality experienced by the end-user.
3.2 Step 2: Specification of quality objects and quality
parameters
3.2.1 List of quality objects and parameters
The definition of quality objects (i.e. criteria formulated in a more general way and
parameters (i.e. measurable, quantified values for the determination of the reached
quality of a service) has been carried out on the basis of an extensive literature survey
in WP 200, the results of which are described in the QUANTIS <strong>Deliverable</strong> 1. In basic
parts the list follows the ISO 21707 standard on “Data quality in ITS systems”.
However this standard deals with data, while QUANTIS considers the full data
processing chain putting emphasis on services. In order to meet this, two more quality
objects have been added, i.e. consistency and relevance.
In QUANTIS the following objects and parameters are considered:
Completeness
Parameters:
o Geographic coverage
o Physical coverage
o Percentage of physical coverage
o Percentage event coverage
o Data types coverage
o Depth of coverage (density)
Availability
Parameters:
o Availability Period
o Up Time
Veracity
Parameters:
o Error probability
o Cross verified
Precision
Parameters:
o Duration accuracy
o Location accuracy
o Content accuracy
o Forecast horizon
Timeliness
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Parameters:
o Data Latency
o Data Update Mode
o Data Update Interval
Consistency of information provided by different media
Relevance
Not all of the quality objects are relevant at all steps of the data processing chain.
While Completeness, Availability, Veracity, Precision and Timeliness are important for
all steps, Consistency and Relevance only affect the carrier information.
The following table gives an overview about the relevance of criteria along the data
processing chain.
Quality
Object
Data Content Service Carrier
information
Completeness + + + +
Availability + + + +
Veracity + + + +
Precision + + + +
Timeliness + + + +
Consistency +
Relevance +
Tab. 3: Relevance of quality objects along the data processing chain
It shall be stressed that although the quality of each of the steps of the data
processing chain has an influence on service quality, in QUANTIS emphasis is given to
the quality analysis at the service level.
This is due to the fact that quality management at data source level basically must be
provided by system operators.
At the content level, quality is difficult to investigate, as algorithms differ from
application to application.
For the given objectives and requirements of QUANTIS in particular the analysis at
service output level has been considered as appropriate, although the approach
generally can be applied at all the steps of the data processing chain.
Relevance denotes how well retrieved information meets the information needs of a
user. There is a relevance of the time, spatial and content domain, all specified in the
assessment methodology. The end-user interface is not covered by QUANTIS.
Therefore, relevance is not broken down into parameters.
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Consistency is the degree to which data are free from variation or contradiction. This is
usually applicable when there is more than one service provider. As the QUANTIS
assessment methodology is provider-based this is not subject to parameter
specifications.
Two different types of parameters are being distinguished.
Quality assessment values (QAV), used for the calculation of the total service
quality, and quantified in terms of quality levels
Descriptive values (non quantified assessment values – NQAV), not quantified
parameters which provide additional quality related information, e. g. update
mode, and which are needed in order to specify the services´ quality
characteristics.
3.2.2 Definition of quality objects and parameters for selected
services
As stated before, it has been considered that the definition of quality objects and
parameters vary for different services, thus must be defined independently.
The following list shows the full list of services for which the quality objects have been
defined in detail by means of quality parameters:
(1) Construction site information
(2) Accidents and warnings
(3) Planned events and calendar
(4) Traffic conditions real time
(5) Traffic conditions predicted
(6) Travel time
(7) Weather information real-time
(8) Weather information predicted
(9) Speed limit information
(10) Co-modal information
The detailed quality parameter definition by service and quality object is provided in
Annex 3.
The quality parameter matrix is a tool to reflect the technical service quality of existing
systems from basic implementations up to cutting-edge technologies.
The detailed definition provided in Annex 3 addresses service level and not user
perception or human factors, nor data collection techniques. Thus, all parameters are
defined accordingly.
For evaluation in terms of the complete effectiveness of systems, it is necessary to also
assess the interface to the end-user, thus to distinguish end-user groups, define
scenarios and to include factors such as accessibility and user acceptance rates etc. At
This point reference shall be given to QUANTIS <strong>Deliverable</strong> 1, Chapter 7.1.
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The quality parameter matrix represents a comprehensive approach for defined quality
objects and Traveller Information services, considering existing expert knowledge as
well as literature. As for many parameters no approaches are available and at the
same time a comprehensive and consistent approach is required for the entire
technical field, harmonisation of thresholds and completion was required and has been
carried out.
The quality parameter matrix will be applied to services of the QUANTIS case studies in
order to test its applicability. Depending of the results obtained during application, an
update might be necessary and will be developed and provided within WP 600.
3.3 Step 3: Definition of quality level thresholds
Quality levels are defined by means of thresholds for each of the quantified quality
parameters.
Five quality levels are distinguished, from level 0 (insufficient quality) to level 4 (high
quality).
The levels are distinguished by the definition of thresholds for the defined parameters
and are also provided in Annex 3.
3.4 Step 4: Weighting of contents, quality objects and quality
parameters and definition of quality levels
The relevance of contents (i.e. core services), quality objects and quality parameters
differs for the different services. Thus, e.g. for the provision of warnings, for instance
about a wrong way driver, “timeliness” is a most important quality object, while this
quality object is not such an important quality object for planned events. Therefore,
weighting values for the quality objects must be defined separately for each service.
In addition to this, it must be considered that a customer oriented service can involve
several core-services, which can vary in terms of their importance for the composed
customer oriented service: e. g. for a traffic situation service, traffic conditions
information and warnings can be regarded as most relevant while event information is
less important.
The total quality value shall reflect the benefits of a service in terms of the set
objectives. Initial values were set. These can be modified later if necessary.
The weighting of contents (core services), quality objects and quality parameters
depends on the purpose of the service. Thus, there are different ways to obtain
weighting factors, e. g. by experts, by service providers or by end-users interviews and
calculation of mean weighting values.
Within QUANTIS for the setting of initial weighting factors an end-user survey has been
carried out with 58 ITS-experienced end-users from:
Germany,
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UK,
Austria and
Finland.
The questionnaire is included in Annex 4.
The questions aimed at:
The estimation of the relevance of quality objects for selected services and
The estimation of the relevance of core-services for the customer oriented
service “traffic situation information”, as the most important composed customer
oriented service in QUANTIS.
The users were asked to distribute %-values according to the importance given to the
different aspects from the personal point of view.
The following tables summarize the mean values obtained from the survey.
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Quality objects
Core service
Completeness
Availability
Veracity
Precision
Timeliness
Event
Construction site 21% 16% 223% 25% 15%
Accidents/warnings 16% 16% 19% 21% 228%
Planned events/calendar 21% 16% 23% 23% 17%
Traffic
conditions
Real-time and predicted 18% 18% 20% 20% 24%
Travel time 19% 17% 23% 23% 18%
Weather Real-time and predicted 16% 16% 24% 24% 20%
Tab. 4: Relevance of quality objects for selected core-services (survey results)
Core Services
Core service
Traffic conditions
Accidents and
warnings
Dynamic traffic situation 28% 28% 19% 13% 12%
Tab. 5: Relevance of core services for dynamic traffic situation (survey results)
Construction Sites
Planned events
Weather
The following diagrams show the results of the questionnaire in detail, indicating the
mean values as well as the standard deviations.
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Importance for Traffic Conditions Information
Mean value and standard deviation
50
45
40
35
30
25
20
15
10
5
0
dynamic traffic
situation
accidents and
warnings
construction sites public events critical weather
situations
Traffic Conditions
Accidents and Warnings
Mean value and standard deviation
50
45
40
35
30
25
20
15
10
5
0
Mean value and standard deviation
50
45
40
35
30
25
20
15
10
5
0
completeness availability veracity precision timeliness
completeness availability veracity precision timeliness
Construction Site Information
Public Events
Mean value and standard deviation
50
45
40
35
30
25
20
15
10
5
0
completeness availability veracity precision timeliness
Mean value and standard deviation
50
45
40
35
30
25
20
15
10
5
0
completeness availability veracity precision timeliness
Weather
Travel Time
Mean value and standard deviation
50
45
40
35
30
25
20
15
10
5
0
Mean value and standard deviation
50
45
40
35
30
25
20
15
10
5
0
completeness availability veracity precision timeliness
completeness availability veracity precision timeliness
Fig. 4: Results of user questionnaires
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3.5 Step 5: Assessment of a customer oriented service by
technical benchmarking
Based on Steps 1 to 4 the quality assessment of a service is carried out by technical
benchmarking. The next steps are the actual “service quality assessment”.
The procedure includes the analysis of each core service involved in the customer
oriented service to be assessed and is done for each of the quality objects and
parameters.
As a basis it is necessary to measure, collect and evaluate suitable operational and real
traffic data, both, long-term and/or samples, according to the defined parameters.
When applying the methodology, the actual sample size of the used input data shall be
provided.
The obtained values are compared to the defined level thresholds of the quality
parameters.
The result of the procedure is a quality level from 0 (insufficient) to 4 (high quality) for
each quality object and service.
3.6 Step 6: Determination of total service quality
Within step 6, the calculation of the total service quality is carried out.
This is done by means of a matrix, as shown in the example of the following figure.
The calculation steps are as follows:
1 Fill in the core services involved
2 Fill in weighting factors of core services (WFCS), according to the relevance
of the core services for the composed customer oriented service. Note:
weighting factors add up to 100%.
3 Fill the weighting factors of quality objects (WFQO), according to the
relevance of the quality object for the core service. Note: weighting factors
add up to 100 % for each core service.
4 Multiply WFCS and WFQO. The obtained factors (TWF) are the total weighting
factors by core service and quality object. (Obtained values add up to 100%)
5 Fill in obtained service quality levels (SQL) 0 to 4, according to the results of
the technical benchmarking
6 Determine Service quality factors (SQF) according to the given table below
(level 4: SQF=1, level 3: SQF=0,9, level 2: SQF=0,8, level 1: SQF=0,7, level
0: SQF=0). Note: if one of the quality objects is “0” the entire core service
obtains “0” (for all quality objects). SQF represents the given service quality.
7 Multiply TWF and SQF and add all values. The result is the total service
quality (TSQ) which considers weighting all factors and reached quality levels.
8 Evaluate TSQ with the given table below (TSQ

Example
Traffic situation real-time (Customer oriented service)
A B C D E F G H
Weighting Factors
Service Quality Assessment
Core services
involved WFCS WFQO TWF SQL SQF TSQ
= B*D E*G
Construction sites 19,00%
Quality objects: Completeness 21,00% 3,99% 4 1,0 3,99%
Availability 16,00% 3,04% 3 0,9 2,74%
Veracity 23,00% 4,37% 3 0,9 3,93%
Precision 25,00% 4,75% 2 0,8 3,80%
Timeliness 15,00% 2,85% 4 1,0 2,85%
Accidents and
warnings 28,00%
Quality objects: Completeness 16,00% 4,48% 3 0,9 4,03%
Availability 16,00% 4,48% 3 0,9 4,03%
Veracity 19,00% 5,32% 2 0,8 4,26%
Precision 21,00% 5,88% 3 0,9 5,29%
Timeliness 28,00% 7,84% 2 0,8 6,27%
Events and
calendar 14,00%
Quality objects: Completeness 21,00% 2,94% 2 0,8 2,35%
Availability 16,00% 2,24% 4 1,0 2,24%
Veracity 23,00% 3,22% 3 0,9 2,90%
Precision 23,00% 3,22% 3 0,9 2,90%
Timeliness 17,00% 2,38% 3 0,9 2,14%
Traffic conditions
real-time 27,00%
Quality objects: Completeness 18,00% 4,86% 3 0,9 4,37%
Availability 18,00% 4,86% 4 1,0 4,86%
Veracity 20,00% 5,40% 2 0,8 4,32%
Precision 20,00% 5,40% 2 0,8 4,32%
Timeliness 24,00% 6,48% 2 0,8 5,18%
Weather
information current 12,00%
Quality objects: Completeness 16,00% 1,92% 4 0,0 0,00%
Availability 16,00% 1,92% 4 0,0 0,00%
Veracity 24,00% 2,88% 0 0,0 0,00%
Precision 24,00% 2,88% 3 0,0 0,00%
Timeliness 20,00% 2,40% 3 0,0 0,00%
Total 100,00% 100,00% 76,78%
Level 2
WFCS Weighting Factor Core Service Level SQF TSQ Level
WFQO Weighting Factor Quality Object
0 0

3.7 Step 7: Definition of target values (optimal service
quality)
The next steps aim at identifying suitable measures for service improvement. It is
summarized in the third part of the matrix, “planning process”.
The full procedure “planning process” is shown in the matrix below.
Example
Traffic situation real-time (Customer oriented service)
A B C D E F G H I J K L
Weighting Factors Service Quality Assessment Planning Process
Core services
involved WFCS WFQO TWF SQL SQF TSQ
Target
SQL
Gap
(if +)
Relative
Importance
Ranking of
actions
= B*D E*G I-F E*J*100
Construction sites 19,00%
Quality objects: Completeness 21,00% 3,99% 4 1,0 3,99% 3 -1
Availability 16,00% 3,04% 3 0,9 2,74% 4 1 3,99 8
Veracity 23,00% 4,37% 3 0,9 3,93% 4 1 3,04 10
Precision 25,00% 4,75% 2 0,8 3,80% 3 1 4,37 6
Timeliness 15,00% 2,85% 4 1,0 2,85% 3 -1
Accidents and
warnings 28,00%
Quality objects: Completeness 16,00% 4,48% 3 0,9 4,03% 3 0
Availability 16,00% 4,48% 3 0,9 4,03% 3 0
Veracity 19,00% 5,32% 2 0,8 4,26% 3 1 5,32 5
Precision 21,00% 5,88% 3 0,9 5,29% 3 0
Timeliness 28,00% 7,84% 2 0,8 6,27% 3 1 7,84 4
Events and
calendar 14,00%
Quality objects: Completeness 21,00% 2,94% 2 0,8 2,35% 3 1 2,94 11
Availability 16,00% 2,24% 4 1,0 2,24% 4 0
Veracity 23,00% 3,22% 3 0,9 2,90% 4 1 3,22 9
Precision 23,00% 3,22% 3 0,9 2,90% 3 0
Timeliness 17,00% 2,38% 3 0,9 2,14% 3 0
Traffic conditions
real-time 27,00%
Quality objects: Completeness 18,00% 4,86% 3 0,9 4,37% 4 1 4,86 7
Availability 18,00% 4,86% 4 1,0 4,86% 4 0
Veracity 20,00% 5,40% 2 0,8 4,32% 4 2 10,8 2
Precision 20,00% 5,40% 2 0,8 4,32% 4 2 10,8 3
Timeliness 24,00% 6,48% 2 0,8 5,18% 4 2 12,96 2
Weather
information current 12,00%
Quality objects: Completeness 16,00% 1,92% 4 0,0 0,00% 3 -1
Availability 16,00% 1,92% 4 0,0 0,00% 3 -1
Veracity 24,00% 2,88% 0 0,0 0,00% 4 4 11,52 1
Precision 24,00% 2,88% 3 0,0 0,00% 3 0
Timeliness 20,00% 2,40% 3 0,0 0,00% 3 0
Total 100,00% 100,00% 76,78%
Level 2
WFCS Weighting Factor Core Service Level SQF TSQ Level
WFQO Weighting Factor Quality Object
0 0

Firstly, target values must be defined on core service level for each of the quality
objects. They show the envisaged quality, according to the operator’s objectives.
According to given priorities, target values might differ for different services, e. g. a
service provider might decide not to include weather information into dynamic traffic
situation information or to allow minor SQL values for specific quality objects of a core
service.
Target levels are compared with the obtained quality levels by subtracting actual SQL
from target SQL.
A positive value means that there is a gap between the target quality level and the
reached quality level, thus improvements are required.
A negative value means that the given quality is higher than the set target and no
improvement will be required.
The multiplication of positive gaps with the weighting factors (TWF) and 100 leads to
obtaining a value indicating the importance of improvements. A larger value correlates
with a higher need for improvement. The values consider both, the extent of the
quality gap as well as the importance of the corresponding quality object.
3.8 Step 8: Cost estimation
For the identified quality gaps and insufficiencies, suitable actions must be identified.
The data processing matrix can help to identify where additional input might improve
the service quality, e. g. by integration of additional data sources.
For each of the considered actions, costs must be estimated. Support for calculating
cost estimations can be found in QUANTIS <strong>Deliverable</strong> 2.
3.9 Step 9: Ranking of improvement actions
The ranking of improvements actions is carried out under consideration of the obtained
relative importance of improvements.
The biggest value is the most important field in which actions are required and so on.
There is also the possibility to rank action fields under additional consideration of
estimated costs. In that case, it might be that not the action field which has been
considered as the most relevant from the action ranking gets priority, as the costs for
improvements in that field might be bigger than in the other fields, ranked lower but
entailing a better cost/benefit relation.
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3.10 Visualisation of quality assessment results by means of
spider diagrams
In addition to the results shown in the quality assessment matrix, it is suggested to
visualize the results by means of spider diagrams to aid with describing the extent of
improvement required based on a pictorial representation of the various core services
quality object levels.
Spider diagrams can be compiled either:
showing the reached quality object levels for the composed service, (calculation
of the reached quality level for each quality object by weighting the reached
quality levels of the core services involved) or
showing the reached quality object levels for each of the different core services
involved.
The following figure shows an example of a spider diagram.
Level 4
Level 3
Level 2
Level 1
crit. Threshold
Completeness
Timeliness
Availability
Precision
Veracity
Fig. 5: Example of a spider diagram, aiming to visualize obtained quality
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4. Acquisition of empiric data from case studies
It is intended to apply the quality assessment scheme for the QUANTIS core services in
order to prove its feasibility. This will be carried within WP 600.
As a basis for its application, operational and real-traffic data will be collected.
The following case studies will be evaluated:
Dynamic traffic situation provided by the Bavarian Traffic Information Agency in
Bavaria
RDS-TMC service output (Ö3), end device test studies in Austria
Weather information services in Finland
4.1 Bavaria: Assessment of dynamic traffic situation
4.1.1 Description of the Bavarian case study
The Bavarian case study in QUANTIS concentrates on the evaluation of the dynamic
traffic information service of the Bavarian Traffic Information Agency, provided on the
internet portal www.bayerninfo.de .
The following picture shows an example for the information provided on the website for
the 12 th of August 2009.
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Fig. 6: Dynamic traffic situation service on the internet portal of the Bavarian traffic
information agency
On the right side of the internet page (Fig. 6), the motorway traffic situation is
indicated in four colours on an interactive map: green for free flowing traffic, yellow for
slow traffic, orange for queuing traffic and red for stationary traffic.
Motorway sections for which current traffic messages (e. g. construction sites) are
available are marked with black lines next to the coloured marked roads.
Motorway sections with critical weather situations are marked with blue.
Furthermore, pictograms indicate specific traffic events on the map, e. g. congestion or
closed lanes. By mouse, additional information can be obtained for the corresponding
message, as shown in the picture.
On the left side of the page there is a list of current traffic messages for the selected
area as visualised on the map.
Future work of the Bavarian Traffic Information Agency will give emphasis to the
realisation of a continuous quality control of the provided services and to implement a
robust and comprehensive quality management system. A basic point will be the
collection of real traffic data in regular intervals, in order to compare the information
provided by the service with the real traffic situation and thus to evaluate the service’s
quality.
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4.1.2 Overview about data collection for quality assessment of
dynamic traffic situation information under
www.bayerninfo.de
Quality
Objects
Parameters Core Service: Dynamic Traffic
conditions
Completeness Geographic coverage Type 1: Bavaria
Physical Coverage Type 2: all motorways
% of physical coverage % of km covered according to physical
coverage
Percentage of event
coverage
Number of reported true events by
number of total events – real traffic data
collection and evaluation – sample: 4h
Data types covered Location, time, effects on traffic, incident
avoidance information if relevant
(warnings) – Examples
Depth of coverage
Density of measuring sites: number of
sites by km network
Availability Availability period Type 3, all year round (24/7)
Up-time Up-time: operational value [%]
Veracity Error probability False events per total reported events
and
Percentage of content provided outside
stated quality boundaries – real traffic
data collection and evaluation – sample:
4h
Cross verified Type 1: no cross-verification
Precision Location accuracy Accuracy in terms of location – real traffic
data collection and evaluation – sample:
4h
Content accuracy Type 3: four categories
Timeliness Data latency Period between event and service
provision – sample 4 h
Data update mode Type 3: periodic
Data update interval Update interval
Tab. 8: Data collection for the quality assessment of bayerninfo.de
4.1.3 Real traffic data collection
For the evaluation of
Completeness – percentage of event coverage
Veracity – error probability and
Precision – location accuracy and
Timeliness – data latency
the data sample collection has been carried out in February 2010.
The comparison of the traffic situation as shown
by the VIB with traffic situation evaluated by means of detector data and
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y GPS data
has been done in order to find out compliance and deviation rates.
Inspection site, time period and sample
The sample collection has been carried out with three GPS-vehicles on the Motorway
ring road of Munich, A 99, from motorway intersection Munich East via motorway
intersection Munich South on motorway A8 to Salzburg until connection point
Rosenheim and back. The total length of the motorway stretch amounts to approx. 130
km. The vehicles will start every 10 minutes and drive along the described stretch.
Start/Ziel
T+R
Fig. 7: Data collection loop on the motorway ring road A99 and the motorway A8 to
Salzburg
The date has been chosen under consideration that quality is most relevant during
critical traffic situations such as congestion, on Friday 19 th of February 2010.
Data has been collected at different steps of the data processing chain and can briefly
be summarized as follows:
- GPS data (speed) to mirror real traffic environment
- motorway detector data from the archive of the motorway TCC
- dynamic traffic conditions information (from the TIC of the Traffic Information
Agency
- Carriers: Screenshots of current traffic situation on the internet portal of the
Traffic Information Agency
The following picture summarizes the sample collection along the data processing
chain.
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Fig. 8: Sample collection along the data processing chain of the Bavarian case study
4.2 Austria: Assessment of RDS-TMC
4.2.1 Description of the Austrian case study
In the Austrian case study, the national RDS-TMC service is assessed. It provides the
following core services:
Construction sites
Accidents and warnings
Real-time Traffic conditions (event based)
Fig. 9 shows that many different stakeholders are partners in or contribute to the
service provision chain. A quality assessment at service level is only possible at the TIC
or TIM server. In the course of QUANTIS the owners of both servers, ASFINAG and Ö3
have been interviewed.
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The data sources for the quality assessment are:
ASFINAG deployment documentation and plans
Austrian Broadcasting Agency website
TMCplus reference receiver (5 stations across Austria)
TMC location code documentation
Interview with ASFINAG/Ö3 experts
Message protocols
eMail
Automatic locating on motorways
eMail
Weather information
Caution notices
Traffic control system
(regulation)
Accidents, congestions outside
the traffic control system,
miscellaneous incidents
Published traffic
information
Published traffic
information
eMail data exchange
neighbours
Published
traffic
information
Consolidated traffic
information for KexEA
Accidents,
congestions
Consolidated traffic
information (DATEX 1a)
Fig. 9: Data sources and data exchange links for the Austrian TMC Plus service
4.2.2 Overview about data collection for quality assessment of
TMCplus service in Autria
Title: Quality assessment of TMCplus service Austria
Date: 19. 02. 2010
Contact: Martin Nemec and Martin Müllner, ASFINAG
Service providers: ASFINAG, Ö3, (ORS)
Core services
Construction sites
Accidents and warnings
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Traffic conditions (only event-based in case of congestion)
Remarks:
All results are valid for construction sites, accidents and warnings,
and event-based traffic conditions.
Traffic condition as well as construction site messages are only
composed, if the Level-of-Service is affected.
Quality parameter Q-Level Description
Geographic coverage 2 whole Austria + DATEX links to Italy (Brennero)
and Germany (Bavaria)
Physical coverage 4 all motorways + federal roads + urban roads
(16 cities)
% of Physical coverage 3 98% coverage of Austrian households according
to Austrian Broadcasting Agency (ORS) – “full
deployment level”.
No other equivalent coverage indicators
available.
Percentage event 3 98% of all detected/reported events can be
TMC coded and are disseminated.
Currently, there is no indication of unreported
events existing, as they are not reported to any
other source either.
Data types covered 3 See data processing chain analysis
Depth of coverage -- Not relevant
Availability period 3 All year round
Up-time 3 5 reference stations spread across Austria have
been installed since July 2009, but no
evaluation has been completed yet.
Currently no target value for TMCplus specified.
Austrian broadcasting network availability is 98-
99%
Error probability
Currently, proper indicators are under
development. No estimation can be given
according to the current definition.
Cross-verified 2 Cross verified via 2 nd source or plausibility
check by human operator
Location accuracy 3 1045 Location codes for 2000km road network
(2km avg. location length)
Data latency 3 1-2 min broadcasting delay, ~30sec editing
Data update interval 3 Event-driven
Tab. 9: Data collection for TMCplus, Austria
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4.3 Finland: Assessment of weather information services
4.3.1 Description of the Finnish case study
The Finnish service case covers the collection and processing of road weather
information and its application in various ITS services.
The main output of the Finnish Road Weather Information Service is the road weather
class which has three possible values: normal, poor, hazardous. It will also be the main
focus of evaluations in the Finnish service case. The quality objects and parameters to
be analysed in the Finnish service case as well as their definitions have been presented
in the table below.
Services: Real-time and forecasted weather information service
Quality Parameters Description
Objects
Completeness Depth of coverage Density of road weather stations per road
kilometre
Veracity Error probability Probability of real-time or forecasted road
weather class matching actual conditions
on a given point on the road network
Tab. 10: Data collection for weather information, Finland
4.3.2 Data collection in the Finnish case study
Data related to the quality of the road weather information services and traffic
management will be collected with a literature study and expert interviews. The
literature study will cover evaluation studies and other material on the subject from
both Finland and other countries. The expert interviews will be focused on the staff of
Finnish road administration and relevant service providers involved in the provision of
road weather information services. Possibilities to estimate error probability of the road
weather class during recent years on the basis of the logs of the Road Weather
Information Service will be studied in WP600.
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References
Öörni, R., Kulmala, R., Newton, D., Scheider, T. & Kellermann, A. (2009):
<strong>Deliverable</strong> 1 of QUANTIS – Definition of key European ITS Services and Data
Types. Vienna, Austria.
Scheider, Newton.... (2009): <strong>Deliverable</strong> 2 of QUANTIS - <strong>Report</strong> on service benefits
and costs.
References for the definition of quality levels:
EasyWay Guideline for core service deployment, Parts A, Bi – Bvi.
EuroRegional monitoring expert group: Data Quality Aspects in EuroRegional
projects, appendix 1.
ITS America, U.S. Department of Transportation: Closing the Data Gap – Guidelines
for Quality Advanced Traveller Information System (ATIS) Data.
Swiss Qualitraffic
COOPERS – Quality assurance and reliability of the processes of traffic control and
traffic information applications
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1. Annex: Matrix for the description of the data
processing chain
2. Annex: Description of the data processing
chain by means of the data processing matrix
for the QUANTIS case studies
3. Annex: Detailed list and definition of quality
objects and quality parameters for selected ITS
services including parameter quantification for
quality levels
4. Annex: End-user survey for the determination
of weighting values for quality objects and
contents
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1. Annex: Matrix for the description of the data processing chain
Time Location
Related
information
Related Information
Location (point)
Location (link)
Location (POI)
Parking places
P+R places
Location (PT stations)
PT lines
Location (area)
Extension
lane(s) affected
time stamp
time horizon
duration
expected impact on traffic
recommendations
warnings
further
Data Collection
Categories
Data Types
Data Sources
traffic detectors
historic traffic data bases
map provider
static speed limit DB
weather detectors
commercial weather services
police
road operator/MCC/Tunnel
road works management
Calendar
Cameras CCTV
PT operators
P+R operator
parking operator
UTC operator
traffic reporting driver
traffic reporting driver GPS
broadcasters
Automatic number plate recognition
FCD
algorithms TCC
road maintenance authority
Telecom operator
Traffic data
traffic volume cars
traffic volume HGV
speed cars
speed HGV
traffic images
Historic traffic data (volume,
speed, travel time)
travel time
Traffic control data
traffic control data (speed limit
data -static and dynamic, signal
control, no passing)
re-routing
ramp-metering
Events
construction sites, road works
Accidents/Incidents
public events, holiday
Points of Intrest
Weather
road surface (temperature, friction)
atmospheric data (temperature,
humidity, precipitation, visibility,
wind)
weather data predictive (surface,
atmospheric, wind)
co-modal data
timetables
disturbances, delays,
cancellations expected
disturbances, delays,
cancellations unexpected
additional PT supply
P+R facilities - static
P+R occupancy real-time
Parking facilities - static
Parking occupancy real-time
Customer Oriented Services
Case Studies
D-Bavaria A FIN UK
routing
dynamic traffic
situation
real-time dynamic traffic
situation
pre-dicted
Traffic situation TMC+
weather services
dynamic journey time
information
event management
Data Congregation
Event
Core Services
Construction sites
accidents/warnings
planned events/calendar
Traffic conditions real-time
Traffic conditions predicted
Travel Time
Weather Information current
Weather Information pred.
Speed limit information
Co-modal information
General Overview
Carriers
RDS / TMC
mobile devices
internet portal
Others
| QUANTIS <strong>Deliverable</strong> 3 - <strong>Report</strong> on detailed data and service quality Page 36 of 52

2. Annex: Description of the data processing chain by means of the data processing matrix for the QUANTIS case
studies
Bavaria - Routing
Related Information
Time Location
Related
information
Location (point) X X X X X X X X X
Location (link) X X X X X X X
Location (POI) X X X X X
Parking places
P+R places
Location (PT stations) X X X
PT lines X X X
Location (area) X X X X
Extension X X
lane(s) affected X X
time stamp X X X X X X X X X X
time horizon X X X X X X X X
duration X X X X X
expected impact on traffic X X
recommendations X X X X
warnings X X X X X
further
X
X
Data Collection
Categories
Data Types
Traffic data
traffic volume cars
traffic volume HGV
speed cars
speed HGV
traffic images
Historic traffic data (volume,
speed, travel time)
travel time
Traffic control data
traffic control data (speed limit
data -static and dynamic, signal
control, no passing)
re-routing
ramp-metering
Events
construction sites, road works
Accidents/Incidents
public events, holiday
Points of Intrest
Weather
road surface (temperature, friction)
atmospheric data (temperature,
humidity, precipitation, visibility,
wind)
Data Sources
traffic detectors X X X X
historic traffic data bases X X
map provider X X X
static speed limit DB
X
weather detectors X X X
commercial weather services X X X
police
X
road operator/MCC/Tunnel X X
road works management
X
Calendar
X
Cameras CCTV
PT operators X X X
P+R operator
parking operator
UTC operator
traffic reporting driver
X
traffic reporting driver GPS
broadcasters
X
Automatic number plate recognition
FCD
X
algorithms TCC
road maintenance authority
Telecom operator
weather data predictive (surface,
atmospheric, wind)
co-modal data
timetables
disturbances, delays,
cancellations expected
disturbances, delays,
cancellations unexpected
additional PT supply
P+R facilities - static
P+R occupancy real-time
Parking facilities - static
Parking occupancy real-time
Customer Oriented
Services
D-Bavaria
Case Studies
routing
dynamic
traffic
situation
real-time
dynamic
traffic
situation
pre-dicted
Data Congregation
Event
Core Services
Construction sites X X
accidents/warnings X X
planned events/calendar X X X
Traffic conditions real-time X X X X X X
Traffic conditions predicted X X X X X X X X X X
Travel Time X X X X X X X X X X X X X X X X X X X X
Weather Information current X X X
Weather Information pred. X X
Speed limit information X X
Co-modal information X X X X X X X X X X X X X X X X X X X X X
Routing, Bavaria
Carriers
RDS / TMC
mobile devices
internet portal
X
X
| QUANTIS <strong>Deliverable</strong> 3 - <strong>Report</strong> on detailed data and service quality Page 37 of 52

Bavaria – Traffic Conditions real-time, predicted
Time Location
Related
information
Related Information
Location (point) X X X X X X X X X X
Location (link) X X X X
Location (POI) X X
Parking places
P+R places
Location (PT stations)
PT lines
Location (area) X X X X
Extension X X
lane(s) affected X X
time stamp X X X X X X X X X X
time horizon X X X X X
duration X X X X X X
expected impact on traffic X X X
recommendations X X X X X X
warnings X X X X X
further
Data Collection
Data Types
Traffic data Traffic control Events Weather Co-modal data
traffic volume cars
traffic volume HGV
speed cars
speed HGV
Data Sources
traffic detectors X X X X
historic traffic data bases
traffic images
Historic traffic data (volume,
speed, travel time)
travel time
traffic control data (speed limit
data -static and dynamic, signal
control, no passing)
re-routing
ramp-metering
construction sites, road works
Accidents/Incidents
public events, holiday
Points of Intrest
road surface (temperature, friction)
atmospheric data (temperature,
humidity, precipitation, visibility,
wind)
X
map provider
static speed limit DB
weather detectors X X X
commercial weather services X X X
police
X
road operator/MCC/Tunnel
X
road works management
X
Calendar
X
Cameras CCTV
PT operators
P+R operator
parking operator
UTC operator
traffic reporting driver
X
traffic reporting driver GPS
broadcasters X X
Automatic number plate recognition
FCD
X
algorithms TCC
road maintenance authority
Telecom operator
weather data predictive (surface,
atmospheric, wind)
timetables
disturbances, delays,
cancellations expected
disturbances, delays,
cancellations unexpected
additional PT supply
P+R facilities - static
P+R occupancy real-time
Parking facilities - static
Parking occupancy real-time
Customer Oriented
Services
D-Bavaria
Case Studies
routing
dynamic
traffic
situation
real-time
dynamic
traffic
situation
pre-dicted
Data Congregation
Event
Core Services
Construction sites X X X
accidents/warnings X X X
planned events/calendar X X X
Traffic conditions real-time X X X X X X X
Traffic conditions predicted X X X X X X X X X X
Travel Time
Weather Information current X X X X
Weather Information pred. X X
Speed limit information
Co-modal information
Traffic Conditions, Bavaria
Carriers
RDS / TMC
mobile devices
internet portal
X
X
X
X
X
X
| QUANTIS <strong>Deliverable</strong> 3 - <strong>Report</strong> on detailed data and service quality Page 38 of 52

Austria – RDS-TMC
Related Information
Time Location
Related
information
Location (point) x x x x x x x x x x x
Location (link) x x x x x x
Location (POI)
x
Parking places
P+R places
Location (PT stations) x x x
PT lines x x x
Location (area) x x x
Extension
x
lane(s) affected x x
time stamp x x x x x x x x x x x x x x
time horizon x x
duration x x x
expected impact on traffic x x
recommendations x x x
warnings x x x x x
further
Data Collection
Categories
Data Types
Traffic data
traffic volume cars
traffic volume HGV
speed cars
speed HGV
traffic images
Historic traffic data (volume,
speed, travel time)
travel time
Traffic control data
traffic control data (speed limit
data -static and dynamic, signal
control, no passing)
re-routing
ramp-metering
Events
construction sites, road works
Accidents/Incidents
public events, holiday
Points of Intrest
Weather
road surface (temperature, friction)
atmospheric data (temperature,
humidity, precipitation, visibility,
wind)
Data Sources
traffic detectors x x x x
historic traffic data bases
map provider
static speed limit DB
weather detectors x x
commercial weather services x x x
police x x
road operator/MCC/Tunnel x x x x x
road works management
x
Calendar
Cameras CCTV x x
PT operators x x x x
P+R operator
parking operator
UTC operator x x x
traffic reporting driver x x x
traffic reporting driver GPS
broadcasters
Automatic number plate recognition
FCD
algorithms TCC x x x
road maintenance authority x x
Telecom operator
weather data predictive (surface,
atmospheric, wind)
co-modal data
timetables
disturbances, delays,
cancellations expected
disturbances, delays,
cancellations unexpected
additional PT supply
P+R facilities - static
P+R occupancy real-time
Parking facilities - static
Parking occupancy real-time
Customer
Oriented Services
A
Case Studies
Traffic situation TMC+
Data Congregation
Event
Core Services
Construction sites x x
accidents/warnings x x x
planned events/calendar
Traffic conditions real-time x x x x x x
Traffic conditions predicted
Travel Time x x x x
Weather Information current x x x
Weather Information pred.
x
Speed limit information x x x x
Co-modal information x x x x
RDS-TMC Austria
Carriers
RDS / TMC
mobile devices
internet portal
x
(x)
(x)
(x)
available but not evaluated
| QUANTIS <strong>Deliverable</strong> 3 - <strong>Report</strong> on detailed data and service quality Page 39 of 52

Finland - Weather
Related Information
Location (point) x*2 x*2 x*2 x*2 *2 forecast is calculated also for all road weather stations
Time Location
Related
information
Location (link) x*5
Location (POI)
Parking places
P+R places
Location (PT stations)
PT lines
Location (area) x*1 x*3 x*3 x*3 x*3 x*4
Extension
lane(s) affected
time stamp
time horizon
duration
expected impact on traffic
recommendations
warnings
probability
*5 attempts have been made to extend the status of road surface measured at one road weather
station to the whole road link; the approach was found to produce sometimes inaccurate results
*1 Foreca uses a specific algorithm to extend point-based temperature data to 2-dimensional area
to visualise it on a map
*3 variable 'level of confidence' describes the expected reliability of the forecast and it can be
attached to area-based forecasts
*4 Warnings issued by FMI to road users are defined on the basis of geographical area; Foreca
has a product which includes point-specific warnings on slippery road surface
*3 *3 *3 *3 *6 *6 Precipitation: probability, state of precipitation (water/snow/hail), probable intensity
Other notes:
Contractors making markings on the road surface utilize weather information: the work cannot be
done if the road surface is not dry
Contractors working with hot asphalt regularly use weather information: working with heated
asphalt is not possible in case of rainy weather
Data Collection
Categories
Data Types
Air temperature
Weather
Data Sources
traffic detectors
historic traffic data bases
map provider
static speed limit DB
weather detectors x*7 x*7 x*7 x*7 x*7 x*7 x*7 x*7 x*7 x*7
Air humidity
Wind speed
Wind direction
Visibility
Precipitation
Friction on the road surface
Temperature of the road surface
Status of the road surface
dew point
commercial weather services x*7 x*7 x*7 x*7 x*7 x*7 x*8 x*8a
police x*10 x*9 x*9a
road operator/MCC/Tunnel x*10
Actions taken by road winter
maintenance operators
Air temperature (forecast)
Air humidity (forecast)
Wind speed (forecast)
Wind direction (forecast)
Visibility (forecast)
Friction (forecasted)
Temperature of road surface
(forecasted)
Status of road surface (forecast)
Precipitation (forecast)
Warnings (ice formation on road
surface)
7 *At present, Finavia and FMI provide their weather data to Finnra. In future, information can
possibly be obtained from Radiation and Nuclear Safety Authority (STUK).
*10 Results of friction measurements performed by other organisations are not available to Finnra
at present (possible in future).
*11 Optical road surface sensor produced by Vaisala measures also the temperature of the road
surface
road works management x*10 x*12
Calendar
Cameras
PT operators
P+R operator
parking operator
UTC operator
traffic reporting driver
traffic reporting driver GPS
broadcasters
Automatic number plate recognition
FCD
Telecom operator
Customer Oriented
Services
FIN
*12 Information about road winter maintenance operations is not utilised nor consistently available.
Monitoring maintenance operations on roads with several lanes may be a complex task.
* 8 Status of road surface forecasted by FMI but not Foreca
* 9 At present, visibility is not forecasted as a quantitative parameter (only qualitative information)
* 8a Foreca has a product which includes forecasts for freezing of road surface and forecast for
snowing (warning product)
* 9a Road condition class is provided as a part of forecast by FMI but not Foreca; Foreca provides
a forecast of freezing of road surfaces; A barrier to forecasting the friction on road surface is the
lack of information about the actions taken by road winter maintenance operators (salting, snow
plowing)
Event
Data Congregation
Core Services
Construction sites
Case Studies
Weather services for road
users (current and
predicted)
weather-related traffic
control (variable speed
limits)
accidents/warnings
planned events/calendar
Traffic conditions real-time
Traffic conditions predicted
Travel Time
Road weather nowcasts and
forecasts for road winter
maintenance contractors * 19
Weather-related variable speed
limits and traffic control x*14 x*15 x*15 * 16
Weather services (current) x*13 x*17 x*17 * 24
Weather services (predicted) * 18 * 25
Speed limit information
Co-modal information
* 19 Information presented to end-users has to be up to date and accurate (for example,
measurement of temperatures) and available when needed (in case of forecasts, short periods of
unavailability are smaller problem because an older but still valid forecast may be available);
Information used to provide a service is usually combined from several sources. In another words,
the more data types are missing and the longer they are not available, the larger the effect on
service quality
* 20 critical or hazardous situations should be detected in all cases (worst case in perspective of
safety is too high speed limit value for hazardous weather and road conditions)
*21 less significant in summertime and at night; availability is important when there are significant
amounts of traffic and when the weather changes
*22 too high speed limit value will increase the speed of traffic flow and be hazardous to road
users, too low speed limit will undermine drivers' confidence to the system
* 23 speed limit value based on outdated information will lead to undesirable consequences such
as undermine drivers' confidence to the system. However, weather does not usually change in
minutes or seconds (usually, only precipitation or fog may change conditions rapidly). Traffic
counts and speeds are monitored in real time, the speed limit value is calculated with
approximately three minute intervals and road weather data used to determine the speed limit
value is usually only three minutes old.
* 24 quality requirements may be different in case of on-trip and pre-trip information. In case of pretrip
information the veracity and completeness of information are most important (timeliness less
significant). In case of on-trip information, timeliness is more important.
* 25 timeliness less critical
*13 Four variables are presented to the end-user in existing Finnra weather and road condition services (air temperature, status of the road surface,
friction on the road surface, temperature of the road surface). TIC operated by Finnra uses all meteorological variables when issuing warnings to road users.
In future, a link-specific road weather information service may be introduced. This service will probably use all variables.
*14 only stations with optical road surface sensor
be lowered on the basis of the combination of dew point and temperature of road surface (relative humidity of air is above 100% and the temperature of
road surface is below 0 degrees; ice forms on road surface).
*15 The value of speed limit is mostly determined on the basis of friction; visibility may also affect the speed limit value in some cases. Speed limit may also
*16 most variable speed limit systems utilizing road weather data in Finland are also controlled on the basis of vehicle speeds and vehicle counts;
objective is to lower the speeds of vehicles and minimise speed differences between vehicles
*17 the most important variables used in service provision are temperature of road surface and dew point
* 18 FMI road weather information service: the warning is issued by FMI, FMI uses data provided by Finnra among other data sources, Finnra has its own
road weather information service (web site). The longer the period of forecasting, the more important are common meteorological variables.
Carriers
RDS / TMC
mobile devices
Radio
www
tv
VMS
extranet
internet portal
Weather, Finland
| QUANTIS <strong>Deliverable</strong> 3 - <strong>Report</strong> on detailed data and service quality Page 40 of 52

United Kingdom - Event
Time Location
Related
information
Related Information
Location (point) X X X X X X X X
Location (link) X X X X X X X X
Location (POI) X X X X X X
Parking places
P+R places
Location (PT stations) X X X
PT lines X X X
Location (area) X X X X X X X X X
Extension X X X
lane(s) affected X X X X X X X X X
time stamp X X X X X X X X X X X X X X X X
time horizon X X
duration X X X X X X X
expected impact on traffic X X X
recommendations X X X X X X
warnings X X X X X
further
Data Collection
Categories
Data Types
Traffic data
traffic volume cars
traffic volume HGV
speed cars
speed HGV
traffic images
Historic traffic data (volume,
speed, travel time)
travel time
Traffic control data
traffic control data (speed limit
data -static and dynamic, signal
control, no passing)
re-routing
ramp-metering
Events
construction sites, road works
Accidents/Incidents
public events, holiday
Points of Intrest
Weather
road surface (temperature, friction)
atmospheric data (temperature,
humidity, precipitation, visibility,
wind)
Data Sources
traffic detectors X X X X
historic traffic data bases X X X
map provider X X X X
static speed limit DB X X X
weather detectors X X X
commercial weather services X X X
police
X
road operator/MCC/Tunnel X X X X X X X X X
road works management X X X X
Calendar
X
Cameras CCTV X X X
PT operators X X X X X X
P+R operator X X X
parking operator X X X
UTC operator X X X X X X X X X
traffic reporting driver X X X
traffic reporting driver GPS
X
broadcasters X X X X
Automatic number plate recognition
X
FCD X X X
algorithms TCC X X
road maintenance authority X X
Telecom operator
weather data predictive (surface,
atmospheric, wind)
co-modal data
timetables
disturbances, delays,
cancellations expected
disturbances, delays,
cancellations unexpected
additional PT supply
P+R facilities - static
P+R occupancy real-time
Parking facilities - static
Parking occupancy real-time
Customer Oriented
Services
UK
Case Studies
dynamic journey time
information
event management
Data Congregation
Event
Core Services
Construction sites X X
accidents/warnings X X
planned events/calendar X X X
Traffic conditions real-time X X X X X X X X X X
Traffic conditions predicted X X X X X X X X X X
Travel Time X X X X X X X X X X X X X X X
Weather Information current X X X X
Weather Information pred. X X
Speed limit information X X X X
Co-modal information X X X X X X X X X X X X X X X X X X X
Event, UK
Carriers
RDS / TMC
mobile devices
internet portal
X
X
X
| QUANTIS <strong>Deliverable</strong> 3 - <strong>Report</strong> on detailed data and service quality Page 41 of 52

United Kingdom – Journey time
Time Location
Related
information
Related Information
Location (point) X X X X X X X X X X X X X X X X X
Location (link) X X X X X X X X X X X X X X X
Location (POI) X X X X X X X X
Parking places X X X X
P+R places X X X X
Location (PT stations) X X X X
PT lines X X X X
Location (area) X X X X X X X X X X X X X
Extension X X X X
lane(s) affected X X
time stamp X X X X X X X X X X X X X X X X X X X X X X
time horizon X X X X X X
duration X X X X X X X X
expected impact on traffic X X X X X
recommendations X X X X X X X X X X
warnings X X X X X
further
Data Collection
Categories
Data Types
Traffic data
traffic volume cars
traffic volume HGV
speed cars
speed HGV
traffic images
Historic traffic data (volume,
speed, travel time)
travel time
Traffic control data
traffic control data (speed limit
data -static and dynamic, signal
control, no passing)
re-routing
ramp-metering
Events
construction sites, road works
Accidents/Incidents
public events, holiday
Points of Intrest
Weather
road surface (temperature, friction)
atmospheric data (temperature,
humidity, precipitation, visibility,
wind)
Data Sources
traffic detectors X X X X X X
historic traffic data bases X X X
map provider X X X X
static speed limit DB
X
weather detectors X X X
commercial weather services X X X
police
X
road operator/MCC/Tunnel X X X X X X X X X
road works management X X
Calendar
X
Cameras CCTV X X X
PT operators X X X X X X
P+R operator X X X
parking operator X X X
UTC operator X X X X X X X X X X X
traffic reporting driver X X X
traffic reporting driver GPS
X
broadcasters X X X X X X
Automatic number plate recognition X X X
FCD X X X
algorithms TCC X X
road maintenance authority X X X X
Telecom operator X Customer Oriented
Services
weather data predictive (surface,
atmospheric, wind)
co-modal data
timetables
disturbances, delays,
cancellations expected
disturbances, delays,
cancellations unexpected
additional PT supply
P+R facilities - static
P+R occupancy real-time
Parking facilities - static
Parking occupancy real-time
UK
Case Studies
dynamic journey time
information
event management
Data Congregation
Event
Core Services
Construction sites X X
accidents/warnings X X
planned events/calendar X X X
Traffic conditions real-time X X X X X X X X X X X X X
Traffic conditions predicted X X X X X X X X X X
Travel Time X X X X X X X X X X X X X X X X X X X X X X X X X
Weather Information current X X X X
Weather Information pred. X X
Speed limit information X X X X X X
Co-modal information X X X X X X X X X X X X X X X X X X X X X X X X X X
Journey Time, UK
Carriers
RDS / TMC
mobile devices
internet portal
X
X
X
| QUANTIS <strong>Deliverable</strong> 3 - <strong>Report</strong> on detailed data and service quality Page 42 of 52

3. Annex: Detailed list and definition of quality objects and quality parameters for selected ITS services including parameter quantification for quality levels
Version: 2010-05-11_Quality Parameter Table_1.9.xls
(QAV) Quality assessment value
(NQAV) Descriptive value
Quality Object
Completeness
QAV
yes: x Parameter Definition
Quantification/Description
of Quality levels and Types
no: o Construction Sites Ref Accidents and Warnings Ref Planned Events and Calendar Ref Traffic Conditions real-time Ref Traffic Conditions predicted Ref Travel Time Ref Weather information - current Ref Weather information - predicted Ref Speed limit information Ref Co-modal information Ref
Specify road dedicated Type 1: dedicated area Type 1: dedicated area Type 1: dedicated area Type 1: dedicated area Type 1: dedicated area Type 1: dedicated area Type 1: dedicated area Type 1: dedicated area Type 1: dedicated area Type 1: dedicated area
Geographic area including % of
o Coverage border numbers covered
Type 2: dedicated area + >0 % Type 2: dedicated area + >0 % of border Type 2: dedicated area + >0 % of border Type 2: dedicated area + >0 % of
Type 2: dedicated area + >0 % of border Type 2: dedicated area + >0 % of Type 2: dedicated area + >0 % of Type 2: dedicated area + >0 % of
Type 2: dedicated area + >0 % of Type 2: dedicated area + >0 % of
of border numbers
numbers
numbers
border numbers
numbers
border numbers
border numbers
border numbers
border numbers
border numbers
Type 3: ded. area + > 50% of Type 3: ded. area + > 50% of border
Type 3: ded. area + > 50% of border
Type 3: ded. area + > 50% of border Type 3: ded. area + > 50% of border
Type 3: ded. area + > 50% of border Type 3: ded. area + > 50% of border Type 3: ded. area + > 50% of border Type 3: ded. area + > 50% of
Type 3: ded. area + > 50% of
border numbers
numbers
numbers
numbers
numbers
numbers
numbers
numbers
border numbers
border numbers
Type 4: ded. area + 100% of Type 4: ded. area + 100% of border
Type 4: ded. area + 100% of border
Type 4: ded. area + 100% of border
Type 4: ded. area + 100% of border
Type 4: ded. area + 100% of border Type 4: ded. area + 100% of border Type 4: ded. area + 100% of border Type 4: ded. area + 100% of
Type 4: ded. area + 100% of
border numbers
numbers
numbers
numbers
numbers
numbers
numbers
numbers
border numbers
border numbers
o
x
x
x
Physical
coverage
% of Physical
coverage
Specify road network
covered
% of km covered
according to physical
coverage
Type 1: main corridor
motorways
Type 1: main corridor motorways Type 1: main corridor motorways Type 1: main corridor motorways Type 1: main corridor motorways Type 1: main corridor motorways Type 1: critical spots on road
network and all motorways
Type 2: all motorways Type 2: all motorways Type 2: all motorways Type 2: all motorways Type 2: all motorways Type 2: all motorways Type 2: critical spots on road
network and all motorways and
major regional roads
Type 3: all motorways + major Type 3: all motorways + major reg. roads Type 3: all motorways + major reg. roads Type 3: all motorways + major reg.
Type 3: all motorways + major reg. roads Type 3: all motorways + major reg. Type 3: critical spots on road
reg. roads
roads
roads
network and all motorways and all
regional roads
Type 4: + urban and
secondary
Type 4: + urban and secondary Type 4: + urban and secondary Type 4: + urban and secondary Type 4: + urban and secondary Type 4: + urban and secondary Type 4: critical spots on road
network and all motorways, regional
roads, major urban roads
Type 1: critical spots on road network
and all motorways
Type 2: critical spots on road network
and all motorways and major regional
roads
Type 3: critical spots on road network
and all motorways and all regional
roads
Type 4: critical spots on road network
and all motorways, regional roads,
major urban roads
Type 1: main corridor motorways
Type 2: all motorways
Type 3: all motorways + major
reg. roads
Type 4: + urban and secondary
Critical value: 75 % Critical value: 75 % Critical value: 75 % Critical value: 75 % Critical value: 0% 2 Critical value: 75 % Critical value: 80% Critical value: 80% Critical value: 75 % Critical value: 75 %
Type 1: unimodal service
Type 2: motorways and main
railways
Type 3: motorways, major reg.
roads and railways
Type 4: motorways, major reg.
roads, major urban roads and
public transport lines
Type 5: entire road and public
transport network
Level 1: >75% Level 1: >75% Level 1: >75% Level 1: >75% Level 1: >0% 2 Level 1: >75% Level 1: >80% Level 1: >80% Level 1: >75% Level 1: >75%
Level 2: >90% Level 2: >90% Level 2: >90% Level 2: >90% 2 Level 2: >50% 2 Level 2: >90% Level 2: >90% Level 2: >90% Level 2: >90% Level 2: >90%
Level 3: >95% 1 Level 3: >95% 1 Level 3: >95% 1 Level 3: >95% 2 Level 3: >75% Level 3: >95% 3 Level 3: >95% Level 3: >95% Level 3: >95% Level 3: >95%
Level 4: >99% 1 Level 4: >99% 1 Level 4: >99% 1 Level 4: >99% 2 Level 4: >90% 2 Level 4: >99% 3 Level 4: >99% Level 4: >99% Level 4: >99% Level 4: >99%
congestion and slow traffic coverage
considering defined thresholds
congestion and slow traffic coverage
considering defined thresholds
corresponds with physical coverage weather conditions influencing traffic
quality
weather conditions influencing traffic
quality
envents influencing traffic on the
co-modal route
Percentage Number of reported true Critical value: 75 % Critical value: 75 % Critical value: 75 % Critical value: 90 % 2 Critical value: 50 % 2 Critical value: 85% Critical value: 85% Critical value: 75 % Critical value: 75 %
event coverage events by number of total
events
Level 1: >75% Level 1: >75% Level 1: >75% Level 1: >90% 2 Level 1: >50% 2 Level 1: 85% Level 1: 85% Level 1: >75% Level 1: >75%
Level 2: >90% Level 2: >90% Level 2: >90% Level 2: >95% 2 Level 2: >70% 2 Level 2: 95% Level 2: 95% Level 2: >90% Level 2: >90%
Level 3: >95% Level 3: >95% Level 3: >95% Level 3: >97% Level 3: >80% Level 3: 98% Level 3: 98% Level 3: >95% Level 3: >95%
Level 4: >99% Level 4: >99% Level 4: >99% Level 4: >99% 2 Level 4: >90% 2 Level 4: 99% Level 4: 99% Level 4: >99% Level 4: >99%
Data types included relevant data Critical value: no information Critical value: no information Critical value: no information Critical value: no information Critical value: no information Critical value: no information Critical value: surface temperature, Critical value: surface temperature, Critical value: no information
Critical value: no information
covered types
air temperature
air temperature
Level 1: location Level 1: location + type Level 1: location Level 1: location + time (start, end) Level 1: location segment + expected Level 1: based on static speed limits
Level 1: basic static speed limits + Level 1: static travel times
LoS + forecast horizon
location
Level 2: location + start, end
and lanes
Level 2: + effects on traffic Level 2: location + time (start, end) Level 2: Lev.1 + effects on traffic Level 2: + cause Level 2: based on static speed limits
+ consideration of historic data on
major roads for long term prediction
Level 1: surface temperature, air
temperature, air relative humidity,
quantity and form of precipitation,
as well as estimate dew point
Level 2: + content of de-icing
chemicals on road, surface freezing
point and road surface condition (at
least in classes dry/moist/wet/icy
Level 1: surface temperature, air
temperature, air relative humidity,
quantity and form of precipitation, as
well as estimate dew point
Level 2: + content of de-icing
chemicals on road, surface freezing
point and road surface condition (at
least in classes dry/moist/wet/icy
Level 2: all static speed limits
including time-restricted etc.+
location+ extension
Level 2: static travel times and
time-tables
Level 3: Lev. 2 + effects on
traffic
Level 3: + lanes affected
Level 3: Lev. 2 + effects on traffic
(effected roads and sections)
Level 3: Lev. 2 + incident avoidance
information: e. g. routes to avoid
identified congestion
Level 3: + start/end - time of LoS
Level 3: based on static speed limits
+ consideration of historic data on
major roads for long term prediction
and dynamic data on major roads for
short term prediction
Level 3: + visibility Level 3: + visibility Level 3: all static speed limits +
information on the existence of
variable speed limits + location+
extension
Level 3: Level 2 + predicted
deviations
Level 4: Lev. 3
+ pre trip relevant information:
e. g. start/end (time)
+ on trip: related information,
e. g. warnings, re-routing,
speed limit
Level 4: + on trip: related information, e.
g. warnings, re-routing, speed limit
Level 4: Lev. 3 + related information,
e.g. event details, re-routing
Level 4: Lev. 3 + historical data add to
the real-time information to enhance
(e.g. traffic hot spots during certain
times collected over months/years).
Level 4: + expected delay (min )
Level 4: Level 3 + consideration of
weather and event data
Level 4: + wind Level 4: + wind Level 4: Level 3 + up-to-date
limits on VMS
Level 4: Level 3 + up-to-date
events for road and public lines
x
Depth of
coverage
Density of measuring
sites
not relevant
for motorways:
not relevant
for motorways:
for motorways:
for motorways:
Critical value: no service at all 10 Critical value: no service at all not relevant not relevant
Critical value: 30 km
Critical value: 30 km
Critical value: 30 km
Critical value: 30 km
Level 1: 20 - 30 km Level 1: 20 - 30 km Level 1: 20 - 30 km Level 1: 20 - 30 km Level 1: 80 road km Level 1: 80 road km
Level 2: 10 - 20 km Level 2: 10 - 20 km Level 2: 10 - 20 km Level 2: 10 - 20 km Level 2: 40 road km Level 2: 40 road km
Level 3: 5 - 10 km Level 3: 5 - 10 km Level 3: 5 - 10 km Level 3: 5 - 10 km Level 3: 10 road km Level 3: 10 road km
Level 4: < 5 km Level 4: < 5 km Level 4: < 5 km Level 4: < 5 km Level 4: 5 road km Level 4: 5 road km
Availability
o
Availability
period
specify period during
which the availability is
defined
Type 1: peak traffic times Type 1: peak traffic times Type 1: peak traffic times Type 1: peak traffic times Type 1: peak traffic times Type 1: peak traffic times
Type 2: working day (24/5) Type 2: working day (24/5) Type 2: working day (24/5) Type 2: working day (24/5) Type 2: working day (24/5) Type 2: working day (24/5)
Type 3: all year round (25/7) Type 3: all year round (24/7) Type 3: all year round (24/7) Type 3: all year round (24/7) Type 3: all year round (24/7) Type 3: all year round (24/7) Type 3: all year round (24/7) Type 3: all year round (24/7) Type 3: all year round (24/7) Type 3: all year round (24/7)
Tpye 4: other, specify Type 4: other, specify Type 4: other, specify Tpye 4: other, specify Tpye 4: other, specify Tpye 4: other, specify Tpye 4: other, specify (e. g. winter) Tpye 4: other, specify (e. g. winter) Tpye 4: other, specify Tpye 4: other, specify
x Up-time Percentage uptime which Critical value: not guaranteed Critical value: not guaranteed Critical value: not guaranteed Critical value: not guaranteed Critical value: not guaranteed Critical value: not guaranteed Critical value: 90% Critical value: 90% Critical value: not guaranteed Critical value: not guaranteed
can be expected during
the availability period
sample size per year
unless otherwise stated
Level 1: >95% 1 Level 1: >95% 1 Level 1: >95% 1 Level 1: >95% 1 Level 1: >95% 1 Level 1: >95% 3 Level 1: 90% Level 1: 90% Level 1: >95% Level 1: >95%
Level 2: >97% Level 2: >97% Level 2: >97% Level 2: >97% Level 2: >97% Level 2: >97% Level 2: 95% Level 2: 95% Level 2: >97% Level 2: >97%
Level 3: >99% 1 Level 3: >99% 1, 8 Level 3: >99% 1 Level 3: >99% 1 Level 3: >99% 1 Level 3: >99% 3 Level 3: 98% Level 3: 98% Level 3: >99% Level 3: >99%
Level 4: >99,5% Level 4: >99,5% 8 Level 4: >99,5% Level 4: >99,5% Level 4: >99,5% Level 4: >99,5% Level 4: 99% Level 4: 99% Level 4: >99,5% Level 4: >99,5%
Veracity
x
o
Error probability Percentage of content Critical value: > 25 % Critical value: > 25 % Critical value: > 25 % Critical value: > 25 % Critical value: > 25 %
Critical value: > 15 % Critical value: > 15 % Critical value: > 25 % Critical value: > 25 %
provided outside stated
quality boundaries:
not relevant - included under
precision (duration accuracy)
false events per total
reported events Level 1: 10 -25% Level 1: 10 -25% Level 1: 10 -25% Level 1: 10 -25% Level 1: 10 -25% Level 1: 10 - 15 % Level 1: 10 - 15 % Level 1: 10 -25% Level 1: 10 -25%
Level 2: 5 - 10% 5 Level 2: 5 - 10% 5 Level 2: 5 - 10% 6 Level 2: 5 - 10% 5 Level 2: 5 - 10% 5 Level 2: 5 - 10% 5 Level 2: 5 - 10% Level 2: 5 - 10% 6 Level 2: 5 - 10% 5
Level 3: 2 - 5% Level 3: 2 - 5% Level 3: 2 - 5% Level 3: 2 - 5% Level 3: 2 - 5% Level 3: 2 - 5% Level 3: 2 - 5% Level 3: 2 - 5% Level 3: 2 - 5%
Level 4: < 2% Level 4: < 2% Level 4: < 2% Level 4: < 2% Level 4: < 2% Level 4: < 2% Level 4: < 2% Level 4: < 2% Level 4: < 2%
Cross verified Indicates whether data Type 1: No cross verification Type 1: No cross verification
Type 1: No cross verification
Type 1: No cross verification Type 1: no cross-verification Type 1: no cross-verification
has been cross verified
with one or more
additional sources
not relevant
not relevant
not relevant
not relevant
Type 2: Cross verification with
one or more sources
Type 2: Cross verification with one or
more sources
Type 2: Cross verification with one or
more sources
Type 2: Cross verification with one or
more sources
Type 2: cross-verification with visual
inspections (cctv)
Type 3: + road winter maintenance
crew reports
Type 4: + floating vehicle data
Type 2: cross-verification with visual
inspections (cctv)
Type 3: + road winter maintenance
crew reports
Type 4: + floating vehicle data
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Quality Object
QAV
yes: x Parameter Definition
Quantification/Description
of Quality levels and Types
no: o Construction Sites Ref Accidents and Warnings Ref Planned Events and Calendar Ref Traffic Conditions real-time Ref Traffic Conditions predicted Ref Travel Time Ref Weather information - current Ref Weather information - predicted Ref Speed limit information Ref Co-modal information Ref
Precision
x
o
x
Location
accuracy
Forecast
horizon
Duration
accuracy
Accuracy in terms of
location
Critical value: >15 km Critical value: >15 km Critical value: no reference Critical value: >15 km Critical value: >15 km Type 1: Between fixed points at
relevant locations (e. g. tunnels,
borders)
Level 1: 10-15 km Level 1: 10-15 km Level 1: reference to geographic area –
50km
Level 2: 5-10 km Level 2: 5-10 km Level 2: reference to more detailed
geographic area (village, districts, part of
town) - < 10km
Level 1: 10-15 km Level 1: 10-15 km Type 2: Between origin and
destination (address, POI,
coordinates)
Critical value: 50 km Critical value: 100 km Critical value: no information Critical value: no geo-reference
Level 1: 20-50 km Level 1: 40-100 km Level 1: default Level 1: textual description of
routes
Level 2: 5-10 km Level 2: 5-10 km Level 2: 10-20 km Level 2: 20-40 km Level 2: static Level 2: textual description + map
of important points
Level 3: 100m - 5 km Level 3: 100m - 5 km Level 3: exact location, address Level 3: 100m - 5 km Level 3: 100m - 5 km Level 3: 5-10 km Level 3: 10-20 km Level 3: dynamic, TMC-accuracy Level 3: Level 2 + route
visualisation
Level 4: exact location
according to GPS or mileage -
15% Level 3: between 25% and >15%
o
Content
accuracy
Accuracy /weakness of
information provided
Level 4: 15 min Level 4: 60 Critical value: after event Critical value: >60 2 Critical value: >15 2 Critical value: 3 minutes Critical value: 5 min Critical value: 5 min Critical value: after speed limit Critical value: 3 minutes
Time period between end
of event and cancellation
Level 1: during site Level 1: 30-60 min Level 1: during event Level 1: 30-60 min 2 Level 1: 10 - 15 min 2 Level 1: 2-3 minutes Level 1: < 5 min Level 1: < 5 min Level 1:not existing Level 1: 2-3 minutes
Level 2: with site start Level 2: 15 - 30 min Level 2: with event start Level 2: 15 - 30 min 7 Level 2: 5 - 10 min Level 2: 1-2 minutes Level 2:

Scope of the QUANTIS quality parameter table
QUANTIS focuses on the assessment of the technical aspects of service quality,
mainly based on the ISO21707 report and other literature. QUANTIS aims also to
provide support for future investments in quality. Therefore, the quality levels as
defined in the quality parameter table have been chosen to reflect the range of system
capabilities, from very basic systems providing basic information services, to state-ofthe-art
technologies.
Please note, that the quality levels do not give any recommendation of service quality.
The QUANTIS methodology is designed in a way to be applied to all stages of the
information chain. Most of the quality parameters chosen will be relevant for all parts
of the information chain, but their definitions, sample collection and levels will change.
In QUANTIS, due to the limiting constraints of the project, the methodology will only
be applied on service output level, not on data acquisition level, service input or on
end-user level. Regarding end-user level additional aspects such as user acceptance
(non-technical) or accessibility can influence the impact severely. Thus, the
assessment methodology would need to be extended.
The QUANTIS methodology is designed to be applied for different kinds of ITS.
However, detailed parameter definition has been carried out for Traveller
Information Systems (TIS). This is due to the fact that all the QUANTIS case studies
involve TIS.
The ten TIS core services, i.e. construction sites, accidents and warnings,
planned events and calendar, traffic conditions real-time, traffic conditions
predicted, travel time, weather information current, weather information
predicted, speed limit information and co-modal information, specified in the
quality parameter table refer to different service contents and correspond with the TIS
services identified in the EASYWAY TIS expert group. This modular approach allows
also the evaluation of services which combine more than one core service.
A multitude of quality parameters have been reviewed. From this, the parameters
listed in the quality parameter table have been chosen under consideration of their
impact on technical service quality and ease of use.
Two different types of quality parameters are distinguished in QUANTIS
QAV (Quality Assessment Value): quantified parameter to be
benchmarked and included into the calculation of the total quality value
according to quantified quality levels and
NQAV (Non Quality Assessment Value): qualitative parameter, providing
basic information about the service’s scope, distinguishing different parameter
types.
The distinction of the different quality levels for QAV has been carried out under
consideration of the expert rating of the EASYWAY TIS-Expert Group, TIS European
Study and the COOPERS project. Starting with the figures provided there, it has been
decided to always define five quality levels per QAV and core service. Accordingly,
additional levels were added where necessary.
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The thresholds for the quality levels have been set based on expert knowledge.
Furthermore, the defined quality levels and parameters of the different services were
compared and adopted where appropriate, so that for similar services (clustering
of services according to parameters) the quality levels were defined in a
uniform way. E. g. physical coverage, uptime, error probability are parameters that
have been defined almost uniformly for all of the services. Exceptions are the levels for
weather services, where distinctive expert interviews were carried out with weather
service providers. No adaptations have been carried out between weather services and
the other specified services.
Some parameters required distinctive definitions for the different services, e. g. data
types covered or data latency.
Some parameters (QAV and NQAV) are not relevant for all core services or are already
covered by another parameter. E. g. QAV “depth of coverage”, according to the given
definition, is only relevant, if data is derived from detectors, thus no quality levels are
provided for Construction sites, Events and calendar, Speed limit information and Comodal
information. Another example is NQAV “Content accuracy”, which for most of
the core services is included under “Data types covered”.
Further explanations concerning single cells of the quality parameter table:
- the specific content of weather services requires distinctive definitions for
several parameters
- traffic conditions predicted: percentage of event coverage thresholds differ from
other services, as even single predictions can be regarded as valuable
information
- data types covered generally vary for the different services
- depth of coverage as defined in the table reflects the density of measuring sites,
thus is defined for services based on detected data
- cross-verification: not relevant for information added manually or information
derived automatically from algorithms solely, e. g. planned events, traffic
conditions predicted, speed limit information and co-modal information
- location accuracy: QAV for all services except travel time
- location accuracy also reflects the scope of available technologies, e. g. TMC and
GPS
- forecast horizon: traffic conditions predicted and travel time: according to typical
model horizons
- forecast horizon for weather information: according to meteorological forecast
periods
- duration accuracy: in hours for longer term information; for travel time in %
- content accuracy: only defined for traffic conditions real-time and predicted:
according to distinguished traffic situations
- data latency varies for the different services
- data update interval: event driven and periodic must be distinguished
Generally, the modifications in quality levels of parameters might be necessary
after first experiences with its application. In QUANTIS, modifications will be carried
out after the quality assessments of WP 600.
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4. Annex: End-user survey for the determination of weighting values for
quality objects and contents (core services)
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- End of document -
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