2 Existing Approaches - WIT Press · pollutants and for all relevant vehicle types) and this data...

Development of a microscopic model for the

emission of pollutants and noise

Uwe Reiter

Centre for Transport Studies, Department of Civil Engineering,Imperial College, Imperial College Road, London SW7 2BUE-mail: [email protected]

Abstract

The provision and the use of road transport have various environmental impacts, some beingquantifiable, others not. Different approaches have been developed over the last years to representthe emission of pollutants and the emission of noise, two important, yet quantifiable impacts.However, most of these approaches are not detailed enough to represent those factors that influencethese impacts most: individual driving behaviour, speed and acceleration profiles of singlevehicles, drivers' reactions to changes in traffic control etc. Most existing approaches deal withaggregated values, using total flow, mean speeds and sometimes mean acceleration values only.

The aim of this paper is to show the need for a microscopic approach. The first part consists in areview of existing approaches and a discussion of their advantages, disadvantages, and applicationareas. The second part describes the principles of the microscopic approach to modelling trafficflow and its environmental impacts. The third part goes into describing the type of empirical datarequired for this approach, discussing difficulties to collect this type of data directly and showingup possible solutions.

1. Introduction

There are different strategies of how to deal with environmental impacts causedby road traffic. Whatever strategy is implemented, there will always be a need toestimate the size or amount of different environmental impacts, to be able toassess effects on human beings, to determine maximum acceptable limits, toassess effectiveness of different types of measures. It is therefore important todevelop models capable of quantifying environmental impacts. Although modelswill not solve environmental problems directly, they are the necessary toolssupporting decisions and can therefore contribute to the solutions.

Transactions on the Built Environment vol 30, © 1997 WIT Press, www.witpress.com, ISSN 1743-3509

404 Urban Transport and the Environment for the 21st Century

2 Existing Approaches

The impact that has been modelled most is the emission of pollutants. Proceduresto represent pollution from road traffic usually consist of a set of different models,that have been developed separately and are then combined into a modelhierarchy. The first step when modelling air pollution from road transport withina part of the road network is to get information about the demand for vehicularmovement in the related area. Demand is expressed as vehicular origin-destinationflows between different zones. These modal OD matrixes form the input for trafficassignment models. Their output is input to traffic flow models that represent flowof vehicles on links and across junctions. Emission models use output from trafficflow models and calculate the amount of pollutants produced. Finally, dispersionmodels estimate concentration of each pollutant at any given location and timeusing locations of pollutants sources, the rates of emissions of different pollutantsand considering meteorological and topographical factors as well as chemicalreactions that occur with pollutants. Because of the uncertainties related torepresentation of dispersion, this paper focusses on emission of pollutants.Existing approaches can be classified into four levels of detail, each level beinga simplification of model approaches on the lower level (Akcelik [1]).

2.1 Basic Models

These most detailed types of models represent emissions at the level of vehicleengine performance. Emission rates are described as functions of vehicle engineload typically represented by parameters like engine speed and necessary force.Engine load is characterised by resistances to the movement, by transmission andother losses. These are described by parameters about the vehicle/engine like theengine's cubic capacity, vehicle weight, parameters about the actual transmission(gear), tyre/road transmission, and parameters about other conditions like gradient,air resistance factor, wind speed, coefficient for rolling resistance.

The necessary input to these models is therefore the exact description ofmovements of individual vehicles, speed profile, acceleration profile andtransmission profile. The traffic flow model required for this type of emissionmodel is of basic microscopic type including representation of gear changebehaviour in addition to representation of parameters of vehicle movement. Thevalues of vehicle movement are translated into the parameters of engine load.Each vector of engine load parameters is then related to specific emission factorsfor the different pollutants in so-called pollutant/engine maps. These engine mapshave to be measured for all vehicle types.

The advantage of this type of model is clearly the very detailed representationof circumstances that cause pollution. With all input parameters available, itallows a quite exact estimation of pollutants emitted and can therefore be used forthe analysis of even minor changes in conditions, like changes in drivingbehaviour, changes in vehicle/engine characteristics. On the other hand, this very


Urban Transport and the Environment for the 21st Century 405

detailed representation is also the most important disadvantage of this model type.First, it requires a large amount of empirical data (e.g. engine maps for allpollutants and for all relevant vehicle types) and this data has to be updatedregularly to represent changes in vehicle fleet. Data measurements of this type areexpensive. Secondly, the representation of vehicle movements needs to be veryprecise, including drivers' gear changing behaviour, which again requiresextensive empirical measurements. Thirdly, the model complexity iscomparatively high (high computing times, increased likelihood for errors).

Suitable application areas are therefore very limited parts of the road network,like one link or one junction. All types of changes can be represented, frombehavioural changes to changes in traffic control.

2.2 Microscopic Models

Here, emissions are described at the level of vehicle movement and are thereforeonly related to parameters of vehicle movement, i.e. actual speed and accelerationvalues, and parameters about external conditions, like weather, gradient. Thedescription of vehicle movement can therefore be simpler, concentrating on speedand acceleration; there is no need for representation of gear change behaviour.Empirical data to calibrate and validate this type of flow model is simpler than forthe above, and is available (e.g. Reiter [2]). The empirical data relating speed andacceleration values to specific emissions needed as input for this type of modelwill be simpler as well, i.e. an aggregation of all possible transmission values atgiven speed/acceleration. One model approach will be described in section 3.

The advantage of this type of model is a simpler representation of drivingbehaviour, which is easier to calibrate and validate, a need for less detailedempirical data relating actual movement to emissions, but a still very preciserepresentation of vehicle movement and of those parameters of vehicle movementthat influence emissions most: speed and acceleration. The still quite extensiveamount of necessary empirical data is the main disadvantage of this type of model.

Suitable application areas are single links, junctions, but also road networks,depending on the available computing power. Still, most type of changes can berepresented, from behavioural changes up to changes in traffic control and trafficmanagement, as well as physical changes to the network.

2.3 Micro/macro or Mesoscopic Models

Micro/macro models form an intermediate step: moving vehicles are still treatedindividually, as are the emission of pollutants from these vehicles, but themovement is described on the basis of more macroscopic relationships. One suchmodel was described by Matzoros/Van Vliet [3]. Four vehicle operating modes aredefined in this model: cruise at constant speed, decelerate, idle, accelerate.Movement of vehicles is expressed in terms of these operating modes rather thanwith exact speed/acceleration profiles. Macroscopic shock-wave theory andqueueing theory are used to determine queues at signalised and at priority



junctions respectively and hence those areas and times with idling vehicles.Emission rates are determined for the four operating modes, assuming constant

values of acceleration and deceleration and constant speed. These emission ratesare then used to calculate total emission of simulated traffic summing all distancestravelled in different operating modes or total times spent in idle mode.

yv m N mf,W = E E Ax,(o) • e (v) E (p) = E E "/ ' f,,

i=l /=! i=l /=! ^

with: j the single vehicle of type i, o the operating mode (cruising,accelerating, decelerating), p the pollutants (CO, HC, NO*, SO?, Pb,PM), AXJ (o) the distance travelled, $ (v) the specific emission ofpollutant p [g/m], w, the idling time [s] and e the specific emission ofpollutant p [g/s].

The advantage of this type of model is a quite simple representation of traffic flowthat still allows to represent different traffic flow conditions along a section ofroad, particularly changes in flow conditions in proximity of junctions includingsimple representation of queueing processes. The required empirical input data ismuch simpler, being just average emission values in the different operating modes.However, to get representative emission values for this approach, extensivemeasurements will still be necessary, that are not really simpler than themeasurements for the microscopic approach. However, the model complexity isreduced with respect to microscopic approaches.

The very simple representation of vehicle movement is also the model'sdisadvantage. Movement is represented by constant and average values, either forspeed (at cruising) or for acceleration/deceleration. Areas under different operatingmodes are just determined macroscopically. This is very simplistic: acceleration,one of the most important factors influencing emissions is simply averaged out.It is therefore not possible to represent effects of changes in driving behaviourwith this approach. The model is also not sensitive to small changes in drivingconditions, for example drivers adaptation to coordination of traffic signals.

Because of lower complexity and lower computing times the model can beapplied to larger networks. It can be applied to estimate major changes in trafficmanagement and control. The estimates will however not be very precise andshouldn't be used to determine emission in absolute terms, but rather for thecomparison of different simulated scenarios.

2.4 Macroscopic Models

In macroscopic approaches traffic flow is described in terms of macroscopicrelationships. Movement of single vehicles is aggregated into traffic volume,traffic density and mean speed. Emissions are usually described as functions oftotal distances travelled at given speeds. Emissions are determined by relating total



distances with specific emission factors. An example for this kind of model is theUK TRRL model, described in Hickman, Colwill [4]. Emissions per m length ofroad are calculated by functions like:

E (CO) = 1.031-q- v°-™ • ID'* [g/m]with q: total traffic flow [veh/h], v: mean traffic speed [km/h]

These models are very simple, representation of traffic flow is minimal. Inputdata can be obtained easily. But these models are not very precise. They are notsuitable for any analysis at local level; representation of traffic at junctions is notpossible; no queueing processes can be modelled. The models are not sensitiveto changes of traffic control or traffic conditions and of course not for minorchanges, like changes in driving behaviour. These models are suitable only formacro analysis of total emissions, e.g for regional planning and for estimationof emissions from links with uniform flow conditions.

3. The Need for A New Microscopic Approach

Three categories of factors have been found to influence the level of emissionsfrom road vehicles: vehicle characteristics, weather and meteorological conditions,and driving conditions. Typical vehicle characteristics affecting emissions areengine type and size, vehicle age and maintenance. Cold weather conditionsinfluence temperature of vehicle's engine when turned on and the time necessaryfor the engine to warm up. This affects the amount of pollutants emitted since coldengines have generally higher emission rates. Detailed assignment and flow modelare necessary to represent exact location of vehicles driving with cold engines.

Speed and acceleration are the factors describing driving conditions and havingthe strongest impact on emissions. Figures 1 and 2 show the influence of speedand acceleration respectively (Colwill et al. [5]). Clearly, speed has a stronginfluence: the emission rates for CO and HC are at maximum for very low speeds(below 20 km/h) and decrease with increasing speed; emission rate for NO%decreases with increasing speed up to a speed between 30 and 50 km/h and thenstarts to increase again. Acceleration rate has an even stronger influence onemission of CO and HC, only a small impact on emission ofNO%. It is at the verylow speeds that speed variations are most critical: minor changes in average speedhere can change emission rate by factor 3 to 4. These are typically the speedsoccurring in congested traffic, or in proximity of junctions with vehiclesaccelerating, decelerating or cruising at low speed. The effect of acceleration onemission is again most prominent in congested traffic with stop-and-gomovements, and in proximity of junctions. Minor changes in acceleration rates canchange emission rate by factor 6 to 12. It is exactly these minor changes in speedsand changes in acceleration rates that are not represented by the averaging modelsof macroscopic and of micro/macro type. Macroscopic models usually use onevalue of mean speed for a whole section of road, and even the micro/macro



models use either only one value for mean speed for cruising mode or one fixedvalue in acceleration mode (e.g. 2.8 ms~* in [3] or 0.75 ms~* in Chiquetto [6]).

i

IO

I03

o15

<D

IC/D

I

x:(4oc_0

'£<L>c

Average speed [km/h] Average speed [km/h]

Figure 1: The influence of speed

I

gO

12*TD>%

Acceleration rate [m/s ] Acceleration rate [m/s ]

Figure 2: The influence of acceleration

A correct representation of emissions in urban traffic requires a detailedrepresentation of flow in proximity of junctions. But even for interurban trafficwith the ever increasing traffic volumes, dense and congested traffic conditionswill become more important. This is why the averaging models are generally not



precise enough. Both of the other two approaches would be suitable. As motivatedabove, the lowest level of detail capable of representing speed and accelerationshould be used. This is the microscopic approach: it has a sufficient level of detail,is simpler, requires less empirical data and less computing time than the basicmodel. Modelled speed and acceleration profiles need to be as close to real worlddriving behaviour as possible. An existing model representing driving behaviouron the level of driver's perception and reaction is therefore suggested. This modelis described below.

3.1 Traffic Flow Model

The models developed at the "Institut fur Verkehrswesen" at Karlsruhe Universityare based on driver's perception of and reaction to changes in distance and speeddifference to surrounding vehicles. The models describe vehicle movements onurban and interurban roads. Its core is formed by two models representing thebasic vehicle movements: the longitudinal and the lateral movement. The firstdeals with the movement on one lane, models are often called car followingmodels. The latter focuses on the movement between lanes, models are called lanechanging models.

3.1.1 Car Following ModelThis part of the model is based on Wiedemann [7]. Driving behaviour is describedas a reaction to driver's perception of distance and speed difference to the leadingvehicle. Four states of interaction between driver and front vehicle are defined,each being related to one basic type of driving behaviour. The first is calleduninfluenced driving behaviour: no leading vehicle is obstructing the ownmovement, the driver is driving at his desired speed or tries to reach it byaccelerating. The second is the closing behaviour, where the driver has perceivedthat he is approaching a slower vehicle in front. He reacts accordingly, trying toreduce his own speed to the speed of the front vehicle. The third is the followingbehaviour, where the driver follows the front vehicle at low speed differences andat a distance near the desired following distance. The driver tries to keep speeddifference and acceleration low. Finally, the emergency braking behaviourrepresents the behaviour in case of emergency situations, with the actual distancebeing lower than the minimum desired distance. The driver will react trying toavoid an accident and to reach the minimum distance again.

The driver will perceive a change in distance or speed difference only if therelated physical stimulus (changes in seen size of the vehicle in front) exceedscertain minimum values. This is represented in the model by separating the fourstates of interaction by so-called perception thresholds. Since the physical stimulusdepends on the distance to the front vehicle (for the size) and on relative speed (forthe velocity of change), the thresholds are defined as functions of distance andspeed difference. They are illustrated in the phase plane (Fig. 3). Differences inperception, reaction and driving abilities between drivers and differences invehicle properties are represented by several random parameters related to each



driver-vehicle-unit chosen from normal distributions. This leads to a naturaldistribution of the calculated values. Driving behaviour is modelled by calculationof instantaneous values of acceleration. The model functions determining drivingbehaviour within the four interaction states again include several randomparameters, representing differences in driving skills and vehicle properties.

+dv

UninfluencedDriving

Closing**"*"'

FollowingBehaviour

Emergency • _ _ _ _Brafcng* \AX SDV

Figure 3: Thresholds of car following model

3.1.2 Lane Changing ModelLane changing behaviour is the result of parallel decisional processes, that haveto be simplified in the model. The lane changing behaviour is represented by asequential algorithm with the decision to change the lane depending on thefollowing three questions: Is a lane change desired (obstruction on the actuallane)? Is the situation on a neighbouring lane and hence a change to that laneadvantageous? Is the movement to the neighbouring lane possible and is it safe?

The decision to change the lane has to be based on human perception again. Itis therefore modelled in a similar way as the car following behaviour. Driver'sestimation of distances and speed differences to vehicles on neighbouring lanesneeded for the decisional process is represented by analogous thresholds as above.This model was originally developed and validated by Willmann [8].

3.2 Model of Environmental Impacts

In the microscopic model environmental impacts, like the amount of pollutantsemitted, have to be related to the actual speed and acceleration values simulatedby the traffic flow model. Specific emission factors have to be defined asfunctions, relating one value of emission to each pair of speed acceleration values.Other environmental impacts that can be directly related to speed and accelerationare fuel consumption and noise. Similar relationships can be used to describe this



as functions of speed and acceleration values.One other effect, that can easily be quantified with a microscopic model, is

congestion. Individual travel times can be derived directly and hence lost timescan be calculated for each individual vehicle, for aggregations of vehicle groupsor for all simulated vehicles.

Effects on safety can only be estimated. It is however possible to defineindicators of risk and use simulation to determine the frequency of situationsexceeding given risk thresholds. This can be used to compare simulation ofdifferent scenarios. Time gap distributions and speed distributions on a linkcutting through a populated area could be used as quantitative measures todescribe community severance. Again this could be used as indicator to measuredifferences between simulated scenarios.

4 Data Requirements for Model of Environmental Impacts

Data has to be gathered for a representative sample of the vehicle fleet and it hasto be updated regularly. The empirical data can be either gained by directmeasurements of emissions at this level of detail or by aggregating more detailedempirical data, e.g. translate engine maps into speed/acceleration values.

Vehicle emissions are measured regularly by different institutions, e.g. newvehicle types are tested before being allowed to enter the market, regular vehicleinspection includes emission check, car manufacturing companies undertakemeasurements during development, TR(R)L, other institutions measure exhaustemissions for research purposes. In most cases, vehicles are tested in drivingcycles, representing typical speed and acceleration profiles (e.g. EC directive91/441/EEC). In some cases emissions have been measured on the road, duringnormal operation within a road network (e.g. Potter, Savage [9]). However, datais rarely published in the microscopic level of detail. Published data are normallyaggregations over vehicle types and driving conditions and/or are not very recent.

5 Possible Solutions to Overcome the Lack of Data

If access to detailed but unpublished emission data is not possible and if regularmeasurements are to expensive than there is a third possibility, to use availabledata and to design methods to disaggregate it back to the level required. Older butmore detailed data can be used to determine principle relationships betweenemissions, aggregated influencing factors (like mean speed) and those otherfactors influencing emissions that are rarely published (like acceleration rate,engine temperature, meteorological and road conditions). If we assume thatqualitatively, the influence of the different parameters on vehicle emissions willnot change, then the general relationships determined from older data can be usedto calculate detailed emission maps for the actual vehicle fleet. The emission ratesof vehicles have decreased over the last decades and will continue to do so as aresult of technological progress and as a response to stricter emission standards.



It can however be assumed, that the progress in vehicle/engine design leading tolower emission rates applies to all driving and environmental conditions andtherefore it is possible to apply general relationships to actual emission data.

6 Conclusion

It is important to use the right level of model detail for a given application. It hasbeen argued that for the proper representation of environmental impacts of roadtraffic, in particular the emission of pollutants, a microscopic approach is optimal:it has the required level of detail and the lowest model complexity for this. Amicroscopic model is currently being developed aiming at the representation ofemission of pollutants first, other impacts will be represented at later stages. Oneessential requirement however is, that all the model parts are based on empiricaldata. The output produced by this microscopic emission model could then be usedas input to existing microscopic models of pollutant propagation, closing the gapbetween the different levels of model detail currently used for the representationof emission and dispersion.

7 References

1. Akcelik R. et al. (1983) A hierarchy of vehicle fuel consumption models,7n#c &?gmefn'Mg aW CWm/, 24 (10), pp. 491 - 495.

2. Reiter U. (1994) Empirical studies as basis for traffic flow models,Proceedings of the second international symposium on highway capacity; 2,pp. 493 - 502.

3. Matzoros A. and D. Van Vliet (1992) A model of air pollution from roadtraffic, based on the characteristics of interrupted flow and junction control,Transportation Research A, 26A (4), pp. 315- 355.

4. Hickman A.J. and D.M. Colwill (1982) The estimation of air pollutionconcentrations from road traffic, TRRL Laboratory Report, 1052.

5. Colwill D.M., A.J. Hickman and V.H. Waterfield (1985) Exhaust emissionfrom cars in service-changes with amendments to ECE Regulation 15, TRRLSupplementary Report, 840.

6. Chiquetto S.et al. (1995) Modelling the effects of transport policies on airpollution, The Science of the total Environment, Elsevier, 169, pp. 265 - 271.

7. Wiedemann R. (1974) Simulation des Strassenverkehrsflusses, Schriftenreihe,8, Institut fur Verkehrswesen, Universitat Karlsruhe (in German).

8. Willmann G. (1978) Zustandsformen des Verkehrsablaufs auf Autobahnen,Schriftenreihe, 19, Inst. fur Verkehrswesen, Univ. Karlsruhe (in German).

9. Potter C.J. and C.A. Savage (1983) A survey of gaseous pollutant emissionsfrom tuned in-service gasoline engined cars over a range of road operatingconditions, Warren Spring Laboratory, LR 447 (AP) M, Stevenage.

10. INFRAS AG (1995) Handbuchfur Emissionsfaktoren des Strassenverkehrs,Version 1.1, Okt. 1995, INFRAS AG, Bern, Switzerland.


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