Floating Car Data as Data Source for Modelling of Air ... - EnviroInfo

EnviroInfo 2002 (Wien) 

Environmental Communication in the Information Society - Proceedings of the 16th Conference 

Copyright © IGU/ISEP, Wien 2002. ISBN: 3-9500036-7-3 

Floating Car Data as Data Source for Modelling of 

Air and Noise Pollution in Regions with High Traffic 

Density 

Bernhard Nowotny 1 , Peter Maurer 2 , Katja Schechtner 1 , Wilfried 

Winiwarter 3 , Jürgen Zajicek 1 

Abstract 

As air and noise pollution models depend on real-time traffic data, the integration of traffic 

information (air and noise emissions) has been recognised as an important step in urban 

areas. As the equipment of fixed measurement stations for traffic, air pollution and 

noise detection is limited by financial constraints information from cars equipped with 

sensors (Floating Cars) supplements information from these sources. The position and 

the speed of cars are acquired by satellite navigation in combination with mobile communication, 

cellular phone tracking systems and long wave positioning. Further knowledge 

of the relation among traffic-related parameters is used to estimate traffic flow in 

the road network. Based on the estimations available models for traffic-related air and 

noise emissions can be applied to calculate emission inventories with an improved spatial 

and temporal resolution. So the development of methods for the detection, storage 

and interpretation of Floating Car data has the potential of improving the information 

basis for modelling of the traffic situation, air and noise pollution in regions with high 

traffic density. 

1. Objectives 

The state of the environment is normally assessed by environmental monitoring systems 

(e.g. air pollution monitoring, noise monitoring, water pollution monitoring). 

Logistic as well as financial limitations reduce the amount of actually available information 

to a number of sites that are then considered representative. Different 

modelling approaches have been developed to estimate the spatial distribution within 

1 arsenal research, Business Area Transport Technologies, Faradaygasse 3, A-1030 Wien, 

email: bernhard.nowotny@arsenal.ac.at , http://www.arsenal.ac.at 

2 arsenal research, Business Area Transport Routes Engineering, Faradaygasse 3, A-1030 

Wien, email: peter.maurer@arsenal.ac.at , http://www.arsenal.ac.at 

3 ARC Seibersdorf research GmbH, Systems Research Division, A-2444 Seibersdorf, email: 

wilfried.winiwarter@arcs.ac.at , http://www.arcs.ac.at

660 

the investigated area [e.g. Loibl et al., 1994]. As emissions of noise and atmospheric 

pollutants from the transport sector may considerably effect the environment, frequently 

the emissions from this sector constitute an input to such models. Emissions 

from transport of passengers or goods are known to vary according to a number of 

traffic-related parameters such as traffic flow, speed, weather situation and specific 

vehicle properties (e.g. vehicle type, hub load). Currently many emission models are 

based on statistical or historical data (e.g. vehicle licences per area and average driving 

time, historical time series of traffic flow) so that the modelling domain is covered 

in part only, which consequently leads to rather high uncertainty. 

The information derived from Floating Car Data (FCD) has already been recognised 

as an important source for traffic related information (mainly speed, calculation 

of travel time). FCD technology has the potential to deliver near real-time data 

which can be applied for traffic control measures as e.g. line management on motorways, 

line control systems, generation of traffic messages for drivers and other traffic 

participants [RHAPIT, 1995 and EUROSCOPE / FOCUS, 1999]. 

The aim of this paper is to demonstrate how existing FCD can be applied to assess 

the parameters required for traffic related environmental modelling. While no results 

from emission modelling are available at this stage, we can demonstrate which approaches 

are ready for application. Thus the described method is a further step towards 

integration of transport and environmental monitoring [see EEA, 2001]. 

2. Detecting Floating Car Data and Generating Traffic Information 

The idea of using Floating Car Data (FCD) to collect traffic data could initiate a decisive 

change in traffic monitoring. This method uses sensors integrated in vehicles 

to collect data mainly of position and speed, but also additional parameters (steering 

wheel angle, road slope, etc.). The metered values are sent to a traffic information 

sub-center with the help of the navigation unit via satellite or via GSM- or GPRS 

mobile phones. 

There are three ways to locate a vehicle: 

1) Satellite navigation (GPS/EGNOS/GALILEO) in combination with 

GSM/GPRS/UMTS data transfer 

Satellite navigation systems have already been implemented for commercial systems 

of haulage companies and other vehicle fleets (e.g. taxicabs). A wider application for 

the generation of traffic information has been tested in several pilot trials in European 

cities [c.f. Offermann, 2001, EUROSCOPE / FOCUS, 1999], but few systems 

evolved beyond the test phase [e.g. RHAPIT, 1995]. The reasons for not entering a 

commercial phase were rather organisational than technical obstacles (e.g. missing 

business cases or agreements in form of public-private partnerships). 

29.08.02, 9202 EI P 074 I2 NowotnyB.doc 

Copyright © IGU/ISEP, Wien 2002. ISBN: 3-9500036-7-3

661 

The information supplied by satellite navigation systems are geographical coordinates 

and direction of the vehicle. The precision of location information depends 

on the satellite navigation system used (currently only GPS is available, EGNOS and 

GALILEO are still in development). 

2) Cellular Phone tracking systems as systems used to run Location Based Services 

(LBS) 

Another method to collect mobility data is “tracking” of mobile phones. The mobile 

phones are called from an information center via a special code, which requests the 

SIM-card of the mobile phone to send back an SMS with the number of the currently 

used radio cell. This cell-ID is linked to the co-ordinates of the base station and supplies 

relatively precise information about the current position of the mobile phone 

depending on the size of the radio cell (several hundred meters in urban regions). 

This position data is collected in fixed time steps. In a similar way as by GPS, the 

travel times in a region can be metered by tracing some mobile phones. Traffic information 

of the current position of the tracked mobile phones is generated by comparing 

historic travel time data with the current travel times. 

3) Long wave positioning using hyperbolic navigation methods 

A combination of long wave positioning and satellite navigation reduces the shadowing 

and reflection effects (urban canyoning) in urban regions (e.g. DATATRAK system). 

Additionally a dGPS-signal is modulated onto the long wave to revise a noisy 

signal to get a more exact position value. 

A realistic image of the current traffic situation can only be produced with a 

minimum of pursuant-equipped vehicles in the investigated road network. The proportion 

of floating cars in the whole vehicle fleet depends on the purpose and the 

security of the result. The recognition of a traffic jam demands a smaller proportion 

than the calculation of an exact average velocity. If a generated traffic message has 

to be confirmed by more than one vehicle on the same road segment the proportion 

(and the correlated probability of a passing vehicle) has to be higher. A pilot trial in 

the Rhine/Main Region [RHAPIT, 1995] states that 2 to 3 % of traffic volume is 

necessary for fast and reliable detection of a traffic disruption. Due to the currently 

low level of pursuant-equipped vehicles this traffic data metering method will only 

be applicable in a few years. The current architecture of a Traffic Information Center 

(TIC) should consider the implementation of new interfaces and algorithms to be 

able to use information from FCD technologies. 

The individual position values arriving from the Floating Cars have to be allocated 

to a virtual metering station in a digital road map stored in a Geographic Information 

System (GIS) before the integration of FCD into a traffic simulation. 

Unlike for stationary metering stations, which collect data for fixed road sections, 

there is the possibility to generate “dummy metering stations” with any desired distance 

in between according to the “traffic sensitivity” of the road section. This per- 



662 

mits a more differentiated view on the current traffic situation. Furthermore the network 

of expensive fixed traffic count devices (loop detectors) can be reduced to a 

minimum which may be necessary for reference purposes. 

As the availability of FCD depends on the presence of specially equipped vehicles 

a feasible vehicle flow will first be reached in urban areas whereas little information 

will be available in rural areas. As most environmental problems related to transport 

occur in urban areas or along routes with high traffic flow (e.g. congestion, peaks of 

air pollution, high noise emissions) the method delivers data precisely where it is 

most urgently needed. 

For the calculation of emissions from the transport sector an estimation of traffic 

flow on a specified road segment has to be available. As FCD mainly deliver position 

and speed of vehicles the parameter traffic flow on a certain road segment has to 

be estimated from further information on the road segment (e.g. number of lanes, 

traffic light phases and development of associated waiting queue, historical and realtime 

data from detectors) and speed. As the vehicle fleet equipped for transmitting 

Floating Car Data is only a representative sample for the whole vehicle fleet the exact 

traffic flow and vehicle classification will still require the use of appropriate loop 

detectors. 

3. Generating Information from Floating Car Data for Status and 

Impact of Transport Related Activities 

3.1 Atmospheric pollutants 

Current approaches to estimate emissions from road traffic all combine an emission 

factor with available information on vehicle mileage [Ntziachristos and Samaras, 

2000]. This very simple approach is complicated by the necessity to separate different 

vehicle types and vehicle concepts (e.g. Diesel vs. gasoline driven cars, with or 

without catalytic converter). Emission factors are also available for different driving 

conditions, ranging from stop-and-go traffic to highway speed traffic. Now the vehicle 

fleet may be determined from car registration numbers, or from occasional as 

well as from continuous traffic counts (loop detectors). Also, vehicle mileage as well 

as temporal distribution of emissions may be estimated from such traffic counts. 

However, especially in cities the number of traffic count sites is relatively small in 

respect to total mileage and requires the use of outputs from sophisticated traffic 

models, if at all available. Additionally, speed distribution is very difficult to assess. 

Vehicle speed is not only required to apply the correct emission factor, but even the 

ratio between different pollutants. Emissions of Nitrogen oxides (NO x ) tend to be 

higher at higher motor temperatures and speeds, compounds emitted due to incomplete 

combustion (carbon monoxide (CO) and volatile organic compounds (VOC)) 

tend to be extremely high during stop-and-go traffic. 



663 

Floating car data deliver information on speed of vehicles and general traffic 

situation. With the help of traffic modelling software, historical data and a small 

number of real-time data from measurement points can be used to estimate traffic 

flow and total mileage throughout such a model area at an adequate temporal resolution. 

Emissions even may be assessed real-time, a feature that is uncommon in standard 

emission inventories. Furthermore, the emission information can be used as input 

to photochemical models. As the chemical regime within city centres is generally 

VOC limited in terms of ozone formation (i.e. further VOC emissions will be critical 

for further photochemical activity – [see Prevot et al., 1997]) additional information 

on the stop-and-go traffic will help to strongly increase reliability of emission inventories 

used for atmospheric modelling or even atmospheric air quality forecasts. 

3.2 Traffic-related Noise 

The main factors for the detection of noise emissions from transport are traffic load 

and composition (fraction of heavy duty vehicles), vehicle speed, road surface, road 

gradient and equipment as e.g. traffic lights. Traffic noise exposure can be calculated 

by appropriate models taking into account these parameters. Additional factors on 

noise generation are the distance of the receptor point from the roadway, height 

above road surface and street or ground morphology, obstacles (e.g. houses) and meteorological 

impacts. 

Floating car data deliver information on speed of vehicles and general traffic 

situation. With the help of traffic modelling software, historical data and a small 

number of real-time data from measurement points can be used to estimate traffic 

load. This forms the basis for calculation of noise emissions where no exact measurements 

are available. 

3.3 Integration of models into simulation software 

The Floating Car Data method delivers basic dynamic information of the current 

traffic situation at different points of times. This method presupposes simulation 

tools using variable time steps instead of constant time steps like the programs offered 

today. Only online simulation can evaluate traffic data sent by moving vehicles 

at different times and positions. 

The air pollution model operating at ARC Seibersdorf calculates emissions with 

time steps of about one hour. Therefore the results of an online traffic simulation 

have to be aggregated to the time steps used in the air pollution model in an interface 

software. An open architecture of this interface between simulation and pollution 

models will assure that changing or new parameters of the air pollution model (time 

steps, ...) can be considered without any technical effort. 



664 

4. Conclusions 

As air and noise pollution models depend on real-time traffic data the integration of 

traffic information with information on air and noise emissions has been recognised 

as an important step in urban areas [see HEAVEN, 2001]. Similar to air pollution the 

implementation of the planned Directive on the Assessment and Management of Environmental 

Noise will require the investigation of the noise situation in urban areas 

[see CALM network, 2002]. As the equipment of fixed measurement stations for 

traffic, air pollution and noise detection is limited by financial constraints the application 

of information from Floating Cars supplements the information from these 

sources. So the development of methods for the detection, storage and interpretation 

of Floating Car data has the potential of improving the information basis for modelling 

of traffic situation, air and noise pollution in regions with high traffic density. 

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