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«Emissions at different conditions of traffic flow J.Veurrnanl, N.L.J. Gense2, I.R. Wilmink3 & H.I. Baarbe4 ‘Transport Research Centre, Ministiy of ...»

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© 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved.

Web: www.witpress.com Email witpress@witpress.com

Paper from: Urban Transport VIII, LJ Sucharov and CA Brebbia (Editors).

ISBN 1-85312-905-4

Emissions at different conditions of traffic flow

J.Veurrnanl, N.L.J. Gense2, I.R. Wilmink3 & H.I. Baarbe4

‘Transport Research Centre, Ministiy of Transport, the Netherlands

2TN0 Automotive, the Netherlands

3TN0 Inro Section Traffic and Transport, the Netherlands 4Ministry of Public Housing, Spatial Planning and the Environment Abstract Although it is widely assumed that congestion causes an increase in exhaust gas emissions, it has always been difficult to quanti~ this relatiomhip. The project Emissions and Congestion investigated this relationship by simultaneously measuring traffic conditions and emissions. Emission factors were derived for different traffic conditions on motorways, ranging from free flow to heavy congestion.

The results clearly indicate that there are significant differences in emissions and fiiel consumption for different types of traffic flow. Heavy traffic dynamics, shortcut traffic, heavy congestion and high speeds lead to significant increases of regulated emissions and fuel consumption of motorway traffic.

Efforts to reduce congestion and traffic dynamics (by traffic management measures) should be concentrated on specific routes or sections with frequent occurrence of heavy congestion and a large share of heavy duty traffic. These are the motorways in the conurbations. Tens of percents of reduction in emissions are possible. The resulting improvements on local air quality can be significant.

Lowering the speed limit to 100 km/h on all sections of Dutch motorways can significantly improve emission levels (most of Dutch motorways have a speed limit of 120 lcrw’h).

1 Why a study of emissions at different conditions?

Last year (2000) the number of Dutch traffic jams increased with another 1000 to the number of 30.000. During peak hours traffic jams are common on major roads, especially in the Dutch conurbation of the Randstad (western part of the © 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved.

Web: www.witpress.com Email witpress@witpress.com Paper from: Urban Transport VIII, LJ Sucharov and CA Brebbia (Editors).

ISBN 1-85312-905-4 Netherlands). Several traffic management measures like peaklanes (use of the hard shoulder), controlled access and dynamic route information panels, have been introduced over the last years, to reduce delays. Despite more intensive use of the existing infrastructure, pricing policy and expansion of infrastructure, congestion will be a common phenomenon in the future as well. Before we started the study there were various opinions about the effects of congestion on exhaust gas emissions and fuel consumption of road vehicles. It was unknown what the effects of congestion and traffic management measures could be on total traffic emissions and what the effects could be on the air quality along motorways. In the Netherlands, we have problems with the air quality targets established in the Framework Directive on Ambient Air Quality at different places along the Dutch motorways in the conurbation of the Randstad. The gaps in knowledge about emissions and congestion were the reason for the Dutch Ministry of Transport, Public Works and Water Management and the Ministry of Housing, Spatial Planning and the Environment to commission TNO for a study about the effects of different conditions of traffic flow on exhaust gas emissions and fuel consumption, by measuring emissions related to real world driving in less or more congested situations.

This paper presents the results of the study. The paper is structured as follows:

In the next section we focus on the set up of the study (2). In the third section the emission factors and the share of each congestion category in the mileage is shown (3). In the fourth and fifth section we will go into the effects of congestion on the local (4) and the national emissions (5). Furthermore we will explore some policy suggestions (6) and desirable follow-up-research (7). Finally we summarise the main conclusions (8).

2 How did we set up the study?

The study consisted of two main elements:

. Deriving emission factors for different real world traffic conditions on motorways, ranging from free flow to heavy congestion.

Calculation of the effects of congestion and the effects of traffic management q measures for specific road sections in the conurbation of the Randstad and for the entire motorway network of the Netherlands.

2.1 Test phase In the first stage of the project, two instrumented cars made a test drive on congested motorways in the conurbation of Western Holland, recording speed, time and traffic conditions. At the same time, tail pipe emissions were measured with on board equipment for purpose of determining the possible differences between measuring directly on the road and measuring in a laboratory. In the laboratory the emissions were measured again while projecting the recorded time/speed pattern of the test drives on a chassis dynamometer. In the laboratory measurements were made simultaneously by the lab measurement equipment and © 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved.

Web: www.witpress.com Email witpress@witpress.com Paper from: Urban Transport VIII, LJ Sucharov and CA Brebbia (Editors).

ISBN 1-85312-905-4 by the in-car-system, in order to be able to differentiate effects caused by the measurement equipment and those caused by driving on a chassis dynamometer.

In order to find a useful categorisation of congestion types on motorways, the data collected during the test drives were analysed. The final categorisation in 10 congestion types is mainly based on average speed and traffic volumes.

Furthermore the traffic profile of the test drive has been compared to the trai%c profile derived from trat%c data available from loop detectors in the road surface. Today, many Dutch motorways, especially those in the conurbations have induction loops in the road surface at regular intervals, measuring average speed and traffic volumes every minute (the Monica-system). The profiles of the test drive were compared to the data from the loops and matched quite well. It was therefore decided that the traffic data from loop detectors could be used to represent traffic conditions.

2.2 Determination of emission factors

In the second stage of the project 30 trips were made with an instrumented car (driven by 5 different drivers) in morning and afternoon peak hours, in order to collect statistical relevant speed/time patterns of traveling on Dutch highways.

The trips were about 60 km long and all situated in the highly congested conurbation of the Randstad. The trips comprises all types of traffic situations form highly congested up to high speed free flow. After statistical compression of the speed/time patterns into 10 trips (one per congestion type), actual emission factors where established by measuring emissions of 20 different passenger cars driving these trips. The relative emission results per congestion type were combined with a huge amount of emission data available from the Dutch “In Use Compliance programme” resulting in emission factors for the average Dutch car.

Using this set up, emission factors for COI, NOX, CO, HC and PMIO were determined for each category of motorway traffic (and one for secondary roads).

For heavy-duty traffic a different approach was used. A previously determined set of factors was adapted to the traffic categorisation used in this study.

2.3 The calculation of congestion effects

In the third stage of the project, a method was developed to calculate the total emissions on specific road sections or routes in the conurbation of Western Holland and on the entire nationaI mo~orway network. Data from the Monicasystem and data from a traffic counting monitoring system (Inweva) formed the basis for the calculation of the travelled kilometres by passenger cars and trucks in each congestion category.

The emissions calculated for the actual situation for road sections and for the entire motorway network were compared to the emissions that would occur in other situations: with less or more congestion, with a speed limit of 100 kdh and, if back roads were used, for parts of the trip.

More about the study set up can be found in (Gense [1] ).

© 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved.

Web: www.witpress.com Email witpress@witpress.com Paper from: Urban Transport VIII, LJ Sucharov and CA Brebbia (Editors).

ISBN 1-85312-905-4 3 Congestion categories and emission factors

3.1 Real world driving emission factors The first two columns of table 1 shows the description of the motorway congestion categorisation as decided on and used in this study. Ranging from heavy traffic congestion to high-speed free flow. Also included is a category for tra!llc on secondary roads (3). With this factor, the effect of drivers avoiding congested motorways by using back roads could also be taken into account.

–  –  –

Table 1: Emission factors per congestion categorisation for passenger cars Table 1 shows the emission factors that were found for passenger cars for the different congestion categories. In general driving in the congestion categories la and 1 b (= stop and go and speeds 40 km/h), back road driving (categorie 3) and high-speed-driving ( 100/120) causes significant increases of emissions and fuel consumption. For example driving in category la (stop and go traffic, average speed e 25 km/h) gives almost twice the amount of C02 compared to the categories 2a12c (average speed 75-100 km/h). Even driving in congestion with an average speed between 25 and 40 km/h gives an increase of about 20~0 CO1.

There are different patterns for the different pollutants. But only NO, emissions is influenced more by average speed rather than by a combination of © 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved.

Web: www.witpress.com Email witpress@witpress.com Paper from: Urban Transport VIII, LJ Sucharov and CA Brebbia (Editors).

ISBN 1-85312-905-4 speed and driving dynamics. Emissions from driving on a back road is rather comparable to motorway congestion (category 1b), except for PMIOemissions, which are quite high on backroads.

Table 2 shows the emission factors for trucks that were derived from previous research. Congestion situations and back road driving doubles the NO. and PMIO emissions, whereas CG increases with about one third.

–  –  –

Table 2: Emission factors per congestion category for trucks and big vans

3.2 Emissions dominated by trucks and old cars It must be mentioned that trucks and cars without a catalytic converter heavily put their mark on the total emission levels. It is noteworthy that truck emissions are significantly higher per vehicle kilometer than the emissions of passenger cars. Especially the C02, NO. en PMIO emissions are dominated by trucks. NO, emissions per kilometer are 10 to 20 times higher than for the average passenger car, depending on the congestion category.

Another remarkable point is domination of the absolute level of emission factors for passenger cars by cars without a catalytic converter. Even though their contribution to the annual Dutch mileage is only 20% and their contribution to the mileage on the motorways is only about 10Yo. (The emission factors from each class of technology is weighted using the share in yearly kilometers driven © 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved.

Web: www.witpress.com Email witpress@witpress.com Paper from: Urban Transport VIII, LJ Sucharov and CA Brebbia (Editors).

ISBN 1-85312-905-4 on the Dutch motorways.) This is caused by the fact that absolute emissions of these cars are 10 to 30 times higher than those of cars equipped with a 3-way catalyst.

3.3 Share of mileage per congestion category After establishment of these emissionfactors total emissions were calculated for (I) specific segments of the Dutch motorways in the conurbation of the Randstad and (H) the entire Dutch motorway network. Figure 1 shows the division over congestion categories (1a-c) as found in the different analyses carried out in the study. The first column shows the congestion categories as recorded by the test vehicles, during the 30 trips in the conurbation of the Randstad. For selected parts of these routes, data has been collected regarding all traffic on these routes, for the periods when the vehicle was present. These division over the categories is shown in the second column. The third column shows the division when the average day (24 hours) on the entire Dutch motorway network is considered.

–  –  –

The share of congested traffic in the total mileage in the conurbation of the Randstad during the peakhours is much lager than when the entire motorway network and the entire day is regarded. In contrast to the situation in the Randstad during peakhours, most traffic on the entire motorway network actually drives under good conditions, given the high shares of categories 2C and 2d.

© 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved.

Web: www.witpress.com Email witpress@witpress.com (rbm Trunsport in k 21s1 Centwy 57’7 Paper from: Urban Transport VIII, LJ Sucharov and CA Brebbia (Editors).

ISBN 1-85312-905-4 4 Improvement of ambient air quality

4.1 Effects on section level

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