The links between micro-scale traffic, emission and … 269.pdf2 Links with micro-scale traffic models 5 2.1 General traffic modelling approaches 5 2.1.1 Junction-based traffic models

The links between micro-scale traffic, emission and air pollution models

by P G Boulter and I S McCrae

PPR 269

PUBLISHED PROJECT REPORT

Transport Research Laboratory

PUBLISHED PROJECT REPORT PPR 269

THE LINKS BETWEEN MICRO-SCALE TRAFFIC, EMISSION AND AIR POLLUTION MODELS

Version: Final

by P G Boulter and I S McCrae

Prepared for: Project Record: Framework Contract no. 3/323-R041

SCOPING STUDY ON THE POTENTIAL FOR INSTANTANEOUS EMISSION MODELLING

Client: Highways Agency (Michele Hackman)

Copyright Transport Research Laboratory, August 2007. This report has been prepared for the Highways Agency. The views expressed are those of the authors and not necessarily those of the Highways Agency.

Approvals

Project Manager T Barlow

Quality Reviewed I McCrae

The linking of micro-scale traffic and emission models Version: Final

2TRL PPR 269

If this report has been received in hard copy from TRL, then in support of the company’s environmental goals, it will have been printed on recycled paper, comprising 100% post-consumer waste, manufactured using a TCF (totally chlorine free) process.

Contents Amendment Record This report has been issued and amended as follows

Version Date Description Editor Technical referee

UPR/IE/146/06 December 2006 Unpublished, approved version P G Boulter I S McCrae

Final 3 August 2007 PPR 269 P G Boulter I S McCrae


3TRL PPR 269

Executive summary Instantaneous emission models aim to provide a precise description of vehicle emission behaviour by relating emission rates to vehicle operation during a series of short time steps (often one second). The complexity of instantaneous models has increased during the last 10 to 15 years. Some instantaneous models, especially the older models, relate fuel consumption and/or emissions to vehicle speed and acceleration during a driving cycle. Other models use some description of the engine power requirement.

In recent years, increases in computing power have enabled more practical use to be made of micro-simulation traffic models. These can be used to assess the effects of various measures applied to the network, such as ramp metering, route diversion, variable speed limits, travel information systems and route guidance, thus leading to improvements in traffic management policies. Such models are the only tools which can be used to assess the impacts of policies and infrastructure changes on individual types of driver, time-varying policies, and complex junctions and layouts.

An essential property of all micro-simulation traffic models is the prediction of the operation of individual vehicles in real time, over a series of short time intervals, and using models of driver behaviour such as car-following, gap acceptance, lane-changing and signal behaviour theories, rather than aggregate relationships. Vehicle operation is usually defined in terms of speed and acceleration for a number of pre-defined or user-defined vehicle types.

The integration of traffic and emission models can be undertaken at various spatial and temporal levels, and at different levels of vehicle aggregation. The level of detail required depends on the objective (e.g. regional transportation planning, assessment of local traffic management measures), and on other constraints such as data availability and computational time requirements. The integration of micro-simulation traffic and emissions models allows simulations to be undertaken at a great level of spatial and temporal detail.

This Report presents an examination of the links between instantaneous emission models, micro-simulation traffic models and air pollution dispersion models. This Report gives most emphasis on the first two model categories, as the integration with air pollution models has received limited emphasis by the modelling community. This integration with air pollution models remains largely restricted by the physical demands on computing power associated with state-of-the-art computational fluid dynamics-based (CFD) models. The advantages offered by instantaneous traffic and emission modelling have not been widely incorporated into air pollution modelling tools.


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Contents

1 Introduction 1

1.1 Background and project objectives 1 1.2 Instantaneous emission models 1 1.3 Model integration 4 1.4 Report structure 4

2 Links with micro-scale traffic models 5

2.1 General traffic modelling approaches 5 2.1.1 Junction-based traffic models 5 2.1.2 Traffic assignment models 6 2.1.3 Micro-simulation traffic models 6

2.2 Specific micro-simulation traffic models 7 2.2.1 VISSIM 7 2.2.2 PARAMICS 10 2.2.3 DRACULA 13 2.2.4 SISTM 14 2.2.5 Summary 16

2.3 Examples of model integration 17 2.3.1 VISSIM-MODEM 17 2.3.2 VISSIM-PHEM 17 2.3.3 VISSIM-CMEM 17 2.3.4 PARAMICS-CMEM 20 2.3.5 PARAMICS-MODEM 20 2.3.6 DRACULA-CMEM 23 2.3.7 SISTM-MODEM 23 2.3.8 Other examples 24

2.4 Comparison of model parameters for UK conditions 24 2.4.1 Road characteristics 24 2.4.2 Traffic flow 25 2.4.3 Vehicle operation 25 2.4.4 Vehicle classification 26

3 Links between emission and air pollution models 29

3.1 Types of air pollution model 29 3.2 Emission and dispersion model integration 30

3.2.1 Models 31 3.2.2 Projects 32

4 Summary and discussion 34

4.1 Linking micro-simulation traffic and emission models 34 4.2 Links between emission and air pollution models 35

5 References 37

Appendix A: Abbreviations and glossary of terms 40


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1 Introduction

1.1 Background and project objectives

TRL has been commissioned by the Highways Agency (HA) to assess the scientific understanding of ‘instantaneous’ emission models for road vehicles. An understanding of emissions helps HA to better target mitigation measures and to develop cost-effective policies for reducing ambient pollutant concentrations in the vicinity of the UK trunk road network.

In theory, the advantages of instantaneous emission models include the following:

• Emissions can be calculated for any vehicle operating profile specified by the model user, and thus new emission factors can be generated without the need for further testing.

• Instantaneous models inherently take into account the dynamics of driving cycles, and can therefore be used to explain some of the variability in emissions associated with given average speeds.

• Instantaneous models allow emissions to be resolved spatially and temporally, and thus have the potential to lead to improvements in the prediction of air pollution. Air quality models typically assume that emissions are evenly distributed along a road section. It is therefore likely that such models will drastically under-predict the emissions and the resulting ambient air concentrations in the vicinity of junctions (Tate et al., 2005).

However, in order to apply instantaneous emission models detailed and precise information on vehicle operation and location is required, otherwise any potential benefits may be lost. Obtaining such information is likely to be rather difficult for many model users as it is relatively expensive to collect. As a consequence, the use of instantaneous emission models has mainly been restricted to the research community. One potential solution to this problem is the use of traffic models, and in particular micro-simulation models, to generate the required emission model inputs.

The overall aims of this project are to review and evaluate instantaneous emission data and models, and to show how improvements in modelling could lead to improvements in the prediction and control of local air quality. The project is divided into four main Tasks:

• Task 1: A review of existing instantaneous emission models for road vehicles. • Task 2: A model evaluation and inter-comparison exercise. • Task 3: An examination of the links between instantaneous emission models, traffic models and air

pollution models. • Task 4: Recommendations for model application and future research.

A separate Report is being compiled for each Task. The review in Task 1 was reported by Boulter et al. (2006), and therefore instantaneous emission models are not described in detail here. Task 2 is reported separately, and thus the relative performance of emission models is also omitted from this report. This Report presents the findings of Task 3, an examination of the links between instantaneous emission models, micro-simulation traffic models and air pollution dispersion models. The Report gives most emphasis on the first two model categories, as the integration with air pollution models is relatively simple. This integration with air pollution models remains largely restricted by the physical demands on computing power associated with state-of-the-art computational fluid dynamics-based (CFD) models.

In the measurement and modelling of vehicle emissions, traffic and air pollution, a number of different abbreviations and terms are often used to describe similar concepts or activities. Appendix A provides a list of abbreviations, and a glossary which explains how specific terms are used in the context of this Report.

1.2 Instantaneous emission models

A range of pollutants are emitted from road vehicles as a result of combustion and other processes. Exhaust emissions of carbon monoxide (CO), volatile organic compounds (VOCs), oxides of nitrogen (NOx) and particulate matter (PM) are regulated by EU Directives, as are evaporative emissions of VOCs. A range of


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unregulated gaseous pollutants are also emitted, including the greenhouse gases carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). However, with the exception of CO2 unregulated pollutants have been characterised in less detail than the regulated ones. Emission levels are dependent upon many parameters, including vehicle-related factors such as model, size, fuel type, technology level and mileage, and operational factors such as speed, acceleration, gear selection, road gradient and ambient temperature. All emission models for road vehicles must take into account the various factors affecting emissions, although the manner and detail in which they do so can differ substantially (Boulter et al., 2006).

Boulter et al. (2006) explained the rationale of instantaneous emission modelling, and described several models with reference to aspects such as availability, cost, capabilities, ease of use and the robustness of the predictions. The review dealt principally with ‘hot’1 exhaust emissions, as most of the instantaneous modelling research relates to this topic. Other sources, which are not normally modelled on an instantaneous basis and were therefore not discussed in detail, include cold-start exhaust emissions, evaporative emissions, and the generation of particles via non-exhaust processes such as tyre wear and brake wear.

Instantaneous emission models aim to provide a precise description of vehicle emission behaviour by relating emission rates to vehicle operation during a series of short time steps (often one second). The complexity of instantaneous models has increased during the last 10-15 years. Some instantaneous models, especially the older models, relate fuel consumption and/or emissions to vehicle speed and acceleration during a driving cycle. Other models use some description of the engine power requirement. However, there are a number of fundamental problems associated with the development of instantaneous models. It is extremely difficult to measure emissions on a continuous basis with a high degree of precision, and then it is not straightforward to allocate the emission values to the correct operating conditions. During measurement in the laboratory, an emission signal is dynamically delayed and smoothed, and this makes it difficult to align the emissions signal with the vehicle operating conditions. Until recently, such distortions have not been taken into account in instantaneous models. Boulter et al. (2006) employed the term ‘unadjusted’ to refer to models in which no adjustments are made to the emission signals to account for dynamic distortion during measurement. Conversely, the term ‘adjusted’ was used to describe models which do attempt to address the distortion. The models described in the review, grouped according to these distinctions, were:

Unadjusted models based on speed and acceleration } MODEM2 (original version)

MODEM (extended version)3 DGV4

Unadjusted models based on engine power }

PHEM5 (heavy-duty vehicle part) VeTESS6 CMEM7

Adjusted models } EMPA model8 PHEM (passenger car part)

The basic characteristics of these models are summarised in Table 1, including details of the supplier of each model, the cost, and the coverage of the model in terms of vehicle categories and pollutants. In practice, the only instantaneous models which are available for the estimation of emission factors are MODEM, CMEM and PHEM. Of these, the model which is most relevant to modern European vehicles is PHEM. 1 Emissions produced when the engine and catalyst are at their full operational temperatures. 2 MODEM = modelling of emissions and consumption in urban areas (Jost et al., 1992; Joumard et al., 1995). 3 Barlow (1997). 4 DGV = Digitised Graz model (Sturm et al., 1994). 5 PHEM = passenger car and heavy-duty mission model (Rexeis et al., 2005). 6 VeTESS = vehicle transient emissions simulation software (Pelkmans et al., 2004). 7 CMEM = comprehensive modal emissions model (Barth et al., 2001a,b; Scora et. al., 2006). 8 Atjay et al. (2005).

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1.3 Model integration

The integration of traffic and emission models can be undertaken at various spatial and temporal levels, and at different levels of vehicle aggregation. The level of detail required depends on the objective (e.g. regional transportation planning, assessment of local traffic management measures), and on other constraints such as data availability and computational time requirements. The integration of micro-simulation traffic and emissions models allows simulations to be undertaken at a great level of spatial and temporal detail.

Cycle average emissions (normally derived using average speed models) are currently used in most air pollution prediction models (Khare and Sharma, 2002). With emission factors of this type, it is not possible to ‘map’ emissions with a high spatial resolution. As discussed earlier, one potential benefit of instantaneous models is that they allow emissions to be resolved spatially, and thus have the potential to lead to improvements in the spatial prediction of air pollution. For this to be possible, there is a need to understand the links between the outputs of instantaneous emission models and the input requirements of detailed air pollution models which allow for air pollution mapping. Such models typically involve the use of computational fluid dynamics (CFD), and examples include MIMO9, ANDREA, OPANA, AVTUNE and DNM. Reviews of these modelling tools are available elsewhere, including reports associated with the European Union HEAVEN10 and OSCAR11 projects.

1.4 Report structure

In Chapter 2 of the Report, consideration is given to the links between micro-simulation traffic models and instantaneous emissions models. The Chapter introduces of a number of different traffic modelling approaches, with particular emphasis on micro-simulation, and describes four specific micro-simulation models in more detail. A summary is then provided of the output parameters from these traffic models and the input parameters required for instantaneous emission modelling. Finally, examples are given of studies in which instantaneous emission models and micro-simulation traffic models have been integrated. Chapter 3 deals with the links between emission models and air pollution models. Finally Chapter 4 provides a summary of the modelling approaches.

9 http://pandora.meng.auth.gr/mds/showlong.php?id=64&MTG_Session=2c8841e4b59539d490f0886abaa92c26 10 http://heaven/rec.org 11 http://www.eu-oscar.org/


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2 Links with micro-scale traffic models

One of the drawbacks of instantaneous emission models is the requirement for detailed input data on vehicle operation to be specified by the user. Few users have the information required, and one potential solution to this is the use of traffic models, in particular micro-simulation models, to generate the required inputs. Traffic modelling and emission modelling are closely-related disciplines, but they have generally been treated separately by researchers and model developers. However, an integrated micro-scale traffic and emissions model would be very useful for studying the effects of traffic on local air quality. This approach would help regulators and vehicle manufacturers to develop strategies or technologies to improve traffic operation for the purpose of reducing exhaust emissions and improving fuel economy.

This Chapter of the Review examines appropriate traffic models, and the links between the input requirements of emission models, and the outputs of traffic models. The factors which need to be considered include:

• The categorisation of road types. • The way in which traffic flows are defined. • The types of operational parameters used (e.g. vehicle speed, gear, engine load). • The systems of vehicle classification.

A combination of literature search and direct approach to model developers has been used to elicit the required information.

2.1 General traffic modelling approaches

At this point it is useful to consider the general types of traffic model which are available. In road traffic models the network is represented by zones, links, nodes and lanes. A zone is the source or sink of traffic where vehicles enter or leave the network. A link is a roadway between two nodes, and consists of one or more lanes. A node is either an external connection to a zone or a junction between links inside the network.

According to Akcelik and Associates (2006), models seldom fall into clear-cut categories. Nevertheless, for the purposes of this Report, it is at least necessary to distinguish between micro-simulation models and other types of traffic model. In fact, the following sections summarise three main types of model - junction-based models, traffic assignment models and micro-simulation models. Given that this Report is concerned with micro-scale modelling, the third type of model is described in the most detail.

2.1.1 Junction-based traffic models

The simplest types of traffic model are those which are used to analyse individual junctions. In most cases, such analyses are undertaken using specialist models, such as the following:

• Priority junctions - PICADY (Priority Junction Capacity and Delay). • Roundabouts - ARCADY (Assessment of Roundabout Capacity and Delay). • Traffic lights - OSCADY (Optimised Signal Capacity and Delay).

- TRANSYT (Traffic Network Study Tool). - LINSIG (Traffic Signal Design Tool for Isolated Junctions and Small Networks).

PICADY, ARCADY, OSCADY and TRANSYT are produced by TRL12. LINSIG is available from JTC consultancy13. With the exception of TRANSYT, these models can estimate, for a given traffic demand and junction configuration, queuing delays and fuel consumption, but the impacts of junction changes on the network as a whole cannot be estimated.

TRANSYT is specifically designed to estimate the impacts on queuing and stopping events of changes in demand and layout for a series of linked traffic signals. The model can estimate the impacts of changes on a network, but travel patterns (i.e. the numbers of trips per time-period between an origin and a destination) and

12 http://www.trlsoftware.co.uk/index.asp?section=Products 13 www.jctconsultancy.co.uk


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the routing of traffic through the network (i.e. the chain of road segments) are fixed. TRANSYT also provides estimates of delays, fuel consumption and emissions for the network under consideration.

Modelling packages such as TRANSYT, ARCADY and PICADY provide an aggregated representation of demand, employing empirical algorithms which are based on data gathered from widely distributed UK sites. It can be difficult to apply these single junction models to complex types of junction, such a signalised roundabouts and gyratory systems, and micro-simulation models have started to be used for such applications.

2.1.2 Traffic assignment models

Where the implications of transport policies and infrastructure changes need to be analysed, it is normal to construct a ‘traffic assignment’ model to analyse changes in traffic flows, delays and emissions on the network. Terms such as ‘macroscopic’ or ‘strategic’ may be used in relation to these models. The models typically deal with traffic flows per hour, although the time period covered may vary from 30 minutes to 24 hours. Assignment models have become rather sophisticated, and now allow the modelling of different vehicle types and large networks with stable results. For most scenarios, the travel pattern is fixed, but the assignment model determines the route by minimising a combination of journey time and cost (known as ‘generalised’ cost). In some cases, the assumption that the travel pattern remains fixed can be relaxed, and the actual travel patterns can be allowed to change as the travel costs on the network change. In other cases, travel demand modelling takes place in a separate model. In some assignment models the travel demand routines are quite sophisticated, employing discreet packets of vehicles but still using the same aggregate delay relationships as junction and assignment models. These are therefore termed ‘mesoscopic’ models, and examples include:

• CONTRAM (CONtinuous TRaffic Assignment Model)14. • SATURN (Simulation and Assignment of Traffic to Urban Road Networks)15. • Cube16. • EMME/217. • VISUM18.

The last three models can also be used to assign public transport passengers to different modes. Such models can be used in conjunction with simple emission factors to estimate emissions over a wide area.

2.1.3 Micro-simulation traffic models

In recent years, increases in computing power have enabled more practical use to be made of micro-simulation traffic models. These can be used to assess the effects of various measures applied to the network, such as ramp metering, route diversion, variable speed limits, travel information systems and route guidance, thus leading to improvements in traffic management policies. Such models are the only tools which can be used to assess the impacts of policies and infrastructure changes on individual types of driver, time-varying policies, and complex junctions and layouts. A number of local authorities in the UK are known to use such models for the detailed examination of relatively small areas (Davison et al., 2002). A useful introductory guide to the principles of micro-simulation traffic modelling has been provided by TfL (2003).

An essential property of all micro-simulation traffic models is the prediction of the operation of individual vehicles in real time, over a series of short time intervals, and using models of driver behaviour such as car-following, gap acceptance, lane-changing and signal behaviour theories, rather than aggregate relationships. Vehicle operation is usually defined in terms of speed and acceleration for a number of pre-defined or user-defined vehicle types. Provided the road network and all its features are represented in the modelling system, accurate representation of real life traffic will depend on the precision in simulating the following (TfL, 2003; Dowling et. al., 2004):

• The arrival at vehicles at the boundary of the modelling domain.

14 http://www.contram.com/news/developments.shtml 15 http://www.saturnsoftware.co.uk/index.html 16 http://www.citilabs.com/ 17 http://www.inro.ca/en/products/emme2/index.php 18 http://www.english.ptv.de/cgi-bin/traffic/traf_visum.pl


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• Car-following - the way vehicles follow those in front and react to changes in speed. • Lane changing - vehicles changing lanes to overtake slower traffic as queues develop ahead, or moving to a

more suitable lane based on turning movement and destination. • Gap acceptance – micro-simulation tools can model complex gap-acceptance situations in urban traffic,

such as filter right-turns at signalised junctions, minor movements at give-way or stop signs, traffic entering priority roundabouts and traffic merging situations.

Modern micro-simulation models tend to have impressive visual displays of outputs. The powerful animations which are offered by most software packages have significant appeal to users. However, current models have a number of limitations (TfL, 2003), including the following:

• Pedestrian modelling is not fully represented. • Although overtaking on single-carriageway roads is common in the real world, this type of manoeuvre is

not expressly catered for. • Some models do not yet have the ability to optimise traffic signal settings. • For networks which are operating at near capacity conditions, slight changes to vehicle arrival patterns can

have significant implications.

• Calibration and validation can be onerous, and large amounts of data can be generated.

Other issues which need to be considered include the time required to generate the input data (network, traffic signals, vehicle and driver characteristics) and calibrate the model, and the potential for the model results to provide a false impression of accuracy - an impressive graphical visualisation of the traffic does not automatically imply an accurate simulation.

A large number of such micro-simulation traffic models have been developed in the past at varying levels of complexity and network size (e.g. in some the network is effectively a single junction) (Liu, 2005b; Hardman and Read, 2006). The different micro-simulation packages currently available vary in their ability to deal with different traffic situations and behaviour. Some of them were developed to deal with motorway corridors and are unable to represent urban traffic behaviour found in urban centres where there is a high level of interaction between different road users. The best-known models in use in the UK are:

• VISSIM (a German acronym for ‘Traffic in Towns – Simulation’). • PARAMICS (PARAllel MICroscopic Simulation). • DRACULA (Dynamic Route Assignment Combining User Learning and micro-simulation). VISSIM and PARAMICS are currently the most widely used packages in the UK. DRACULA, which is closely linked to SATURN, has also been applied in the UK. Besides these packages AIMSUN19, HUTSIM20, SISTM, TRAF-NETSIM and FRESIM, aaSIDRA and aaMOTION have been used (TfL, 2003; Abbott et. al. 2000; Akcelik and Beslry, 2003).

2.2 Specific micro-simulation traffic models

The four main micro-simulation traffic models which are currently used in the UK – VISSIM, PARAMICS, DRACULA and SISTM - are discussed in more detail below.

2.2.1 VISSIM

VISSIM is a commercial traffic micro-simulation model which was developed by PTV AG Karlsuhe, Germany21, with add-ons being provided by various research institutions. VISSIM version 4.00 was released in 2004. The launch of VISSIM version 4.10 was scheduled for early 2005.

The model is aimed at the technical staff responsible for signal control, public transport operators, city planners and researchers to evaluate the influence of new traffic measures and vehicle technologies. The

19 Advanced Interactive Microscopic Simulator for Urban and Non-urban Networks. http://www.aimsun.com/site/ 20 http://www.tkk.fi/Units/Transportation/HUTSIM/ 21 http://www.english.ptv.de/cgi-bin/traffic/traf_vissim.pl


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model’s primary area of application is the detailed modeling of traffic flows on urban networks, and it can be used in the study of traffic behaviour in relation to a wide range of options, including: • Ramp metering. • Bus priority and HOV lanes. • Road layout and junction configuration. • Signal control strategies at complex junctions. • Toll plazas. However, VISSIM is not suitable for corridor capacity improvements at the regional level, or for evaluation of network-wide effects of traveler information/guidance systems.

Four separate models are integrated in one software suite to cover traffic demand, route choice, traffic flow and pollutant emissions. The traffic demand model follows a behavior-oriented, disaggregated approach. It computes the set of trip chains performed during one day in the analysis area. The dynamic route choice is calculated by an iterated simulation of the entire day. Each individual vehicle travels through the road network using the microscopic traffic flow model of VISSIM (Fellendorf and Vortisch, 2000).

The traffic flow model of VISSIM is discrete, stochastic22 and micro-scale, with driver-vehicle units (DVU) being defined as single entities. The default ‘vehicle types’ in VISSIM are cars, LGVs, HGVs, buses, trams, bicycles and pedestrians. New vehicle types can be created, and the existing ones can be modified, and vehicle types can be sub-divided by ‘vehicle class’. For example, different vehicle models can be defined according to length, width, occupancy and acceleration characteristics. The weight and power of HGVs can be defined as distributions. For each HGV VISSIM computes the power:weight ratio (range = 7-30 kW t-1). Both the weight and power values affect driving behaviour on slopes. Model year and mileage distributions can also be defined, as can the temperature of the coolant and catalyst (all these parameters are used in the internal emissions module) (PTV, 2005).

Individual driver behaviour is simulated in discrete time steps using a car-following model for longitudinal vehicle movements, and a rule-based algorithm for lane-changing movements. The dynamic data used include (i) the desired speed distribution, (ii) the desired and maximum acceleration and deceleration, (iii) the traffic composition (the percentage of HGVs), (iv) the traffic volumes entering each link and (v) routing decisions. Vehicles follow each other in an oscillating process. Each vehicle is allocated a desired speed, a desired acceleration and a gap acceptance. If a vehicle’s speed is below its desired speed, it will accelerate to the desired speed using the maximum possible acceleration for the actual speed and vehicle type. As a faster vehicle approaches a slower vehicle on a single lane it has to decelerate. Should the desired gap distance be too small, the vehicle will react to avoid an accident by a sharp reduction in speed. On multi-lane links vehicles check whether they can increase their speed by changing lanes. If so, they seek acceptable gaps on neighbouring lanes. For each driving mode (free driving, approaching, following and braking) the acceleration is described as a result of speed, speed difference, distances, and the individual characteristics of driver and vehicle (PTV, 2005).

The network is created by combining links, which are characterised by their type, the number of lanes in each direction and the width of the lanes, with connectors to replicate turning movements. Other roadway infrastructure, such as traffic signals, priority rules or reduced-speed areas are also available. There are no limits on the size of the network to be modeled, but the practical limit is 60 signalised junctions, and most applications involve between around 4 and 30 junctions. The modelled network usually covers an area of 1-5 km2, or a corridor up to 10 km long.

Data such as network definition of roads and tracks, technical vehicle and behavioural driver specifications, car volumes and paths, transit routes and schedule are entered graphically through dialogue boxes in Windows. A large range of output files are available, both on a vehicle and a link basis and for any specified time interval. Each link is split into segments, for which information on traffic volume, traffic density and traffic speed are available as a function of distance. However, this implies very large amounts of output data, and the post-model data processing for an external emissions sub-model needs to be efficient. Such an approach has been developed for a VISSIM model of the South Yorkshire motorway network (McCarthy,

22 A stochastic event is based on random behaviour. The occurrence of individual events cannot be predicted, although the distribution of all observations usually follows a predictable pattern.


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2003). Animations of vehicle movements – an example is shown in Figure 1 - greatly facilitate the understanding of the impacts of alternative scenarios.

A large amount of time is required to code the VISSIM input data. Most of the effort stems from the requirement of the link/connector scheme to represent in detail the junction layouts. Also, the interface and coding of detector/signal logic for signal control (other than fixed-time plans) may require significant effort

Figure 1: Example of VISSIM visualisation.

The VISSIM emission module requires an additional licence. The modelling routines in VISSIM have been described in a number of PTV papers (e.g. Kohoutek et al., 1999; Fellendorf and Vortisch, 2000), and further information is provided in the manual (PTV, 2005). From the traffic model the type, position, speed and acceleration of every vehicle is defined for each second. This information is used to calculate the instantaneous fuel consumption and hot emissions of each vehicle based on emission maps. The database of engine maps was provided by TUV Rhineland, and was produced during a comprehensive research project sponsored by the German Federal Environmental Agency (Hassel et al., 1994). Steady-state engine maps are used define the rate of emission of CO, HC and NOx from passenger cars and HGVs as functions of the instantaneous values of speed and acceleration. To improve the accuracy of the emission maps, ‘dynamic correction factors’ are applied. These factors depend on kinematic variables such as the number of changes from positive to negative accelerations, idling portions and mean acceleration values.

In addition, the model is capable of considering additional emissions during the warm-up (cold start) phase of the engine as well as evaporation emissions during parking. The Research Group of Volkswagen AG developed a model for cold start emissions based on two thermodynamic sub-models for the engine and the catalytic converter (Kohoutek et al., 1999). The vehicle model in the traffic flow simulation is extended to cover also the temperature of the engine and the catalytic converter, so that these values are known every second. A functional dependency was derived to describe the modified emission behaviour during warm-up conditions. Since the trip chains of the vehicles are known from the transport demand model, it is possible to model emissions with high resolution in time and space during all phases of the trips, including evaporation emissions during parking. A recent enhancement to the model is sensitivity to road gradient, so it can better model truck performance on grade-separated interchanges. This feature also ought to be useful for emission modelling purposes.

In the module the emissions characteristics of each vehicle type are defined via a dialogue box. The attributes


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used for this purpose are listed in Table 2. Emission maps are provided for a given vehicle type and pollutant. The pollutants covered include benzene, CO, CO2, THC, NMHC, NOx, PM, SO2 and evaporative VOCs. Fuel consumption can also be calculated. It is also possible for the user to define their own engine map files. There are three different formats for hot emissions, depending on the vehicle emission category specified in the emission layer, and one format for cold-start emission.

Table 2: Attributes used to define the emission behaviour of a vehicle type (PTV, 2005).

Attribute Description Attribute Description

Category Emission category (e.g. hot, cold) Front2 Frontal area coefficient 2 Name Vehicle type name Coeff_gw Gross weight coefficient Concept Engine description Gear Gear ratio (-) Displacement Engine displacement description Axle Axle ratio (-) Strokes Number of engine strokes Revs Average engine speed (min-1) Energy Energy (fuel) type Nom revs Average nominal engine speed (min-1) Comment Emission comment Torque Average torque (N m) Gearbox Gearbox type (manual/automatic) Powertrain Average powertrain efficiency (-) Displacement Average engine displacement (cm3) Rim Average wheel rim size (inches) Power Average nominal power (kW) Tyre Average tyre size (mm) Length Average length (cm) Wheel Average wheel ratio (m) Net weight Average net weight (kg) Roll1 Rolling resistance coefficient 1 (-) V-Max Average maximum speed (km h-1) Roll2 Rolling resistance coefficient 2 (-) Mileage Average annual mileage (km year-1) Inertia moment of inertia (kg m²) Cw*A Air drag coefficient CPower Power consumption of auxiliaries(kW) Front1 Frontal area coefficient 1

For hot emissions there are three different emission map formats, depending on the vehicle type (motorcycles, passenger cars/light-duty vehicles, and heavy-duty vehicles). The emission maps for motorcycles simply relate emissions (in g km-1) to average speed (in km h-1). The maps for passenger cars and light-duty vehicles are formatted as matrices in which emissions are related to speed (km h-1) and the product of speed and acceleration (in m2 s-3). The emission data are stated in mg s-1. The emission maps for heavy-duty vehicles are also formatted as matrices. In this case the independent variables are normalised engine speed and normalised power, with the emission data being stated in g kW-1 h-1.

The emission maps for cold-start emissions are formatted as matrices which are independent of the vehicle emission category specified according to vehicle type. However, either the catalytic converter temperature or the cooling water temperature must be used to access the emission data. The emission factors are stated in g kW-1 h-1, and as a function of the temperature class (in °C) and the normalised power.

The emissions functions and models used in VISSIM do not appear to be available independently.

2.2.2 PARAMICS

PARAMICS is a suite of software tools for microscopic traffic simulation. The history of PARAMICS is rather convoluted. The original software was developed in the late 1980s, and since 1998 PARAMICS has been marketed by two companies – Quadstone and SIAS - with mutual territorial agreements (Luk and Tay, 2006). SIAS originally had the UK rights, but since August 2005 both companies have been able to sell PARAMICS in the UK. Quadstone, along with its version of PARAMICS, was sold to another commercial software company in December 2005. The Quadstone and SIAS models are known as Q-PARAMICS and S-PARAMICS respectively, and according to SIAS the two products are sufficiently different for them to be known by different names. However, both models appear to be built upon very similar principles, with the main differences relating to functionality and visualisation, but such differences are beyond the scope of this Report. The following paragraphs provide a small amount of additional information on the two models.


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Q-PARAMICS

The latest version of Q-PARAMICS is 5.2. The Q-PARAMICS suite23 contains the following nine modules:

23 http://www.paramics-online.com/home/home.htm

• Converter • Modeller • Estimator

• Processor • Analyser • Programmer

• Viewer • Designer • Monitor

Modeller is the core Q-PARAMICS simulation tool. It is used for model building, traffic simulation, visualisation and statistical output through a graphical user interface. Modeller is a fully scalable tool; it can be used to model an entire city’s traffic system or a single junction. Converter takes existing network data from a range of sources and converts them into a basic Q-PARAMICS network. It allows the user to teach the application how data should be interpreted. Estimator is an origin-destination matrix estimation tool. Users can interact directly with the estimation process as it runs, avoiding the need for long cyclic estimation processes. Processor is a batch simulation tool used for sensitivity and option testing, and the Analyser is used for custom analysis and reporting of model statistics. Programmer is a comprehensive development application programming interface (API). It allows users to augment the core Q-PARAMICS simulation with new functions, driver behaviour and practical features. Researchers can opt to override or replace sections of the core simulation with their own behavioural models. Viewer is a visualisation and demonstration tool, and Designer is a 3-D model building and editing tool provided for use with Modeller and Viewer.

Q-PARAMICS provides a range of visualisation and presentation tools. The graphics tools help with network building, understanding driver behaviour, calibration, validation, and real-time statistics feedback. An example screen view from the Modeller module is shown in Figure 2. In addition, Q-PARAMICS provides 3-D visualisation of the virtual world, including realistic vehicle and building shapes, weather effects and terrain mapping.

Figure 2: Q-PARAMICS Modeller interface.

Monitor is the module which seems to be the most relevant to this Report. It is a pollution evaluation module which integrates directly with the core simulation. Monitor can be used to collect pollution data at the per-vehicle level. At present, PARAMICS uses simple look-up tables of exhaust emissions and fuel consumption as a function of vehicle type, speed, and acceleration. Currently, there are only default tables for a single


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vehicle type. In order to make this a more powerful model, data sets for a variety of vehicles must be provided. Currently development is underway to improve the modelling of exhaust emissions.

The program works using a default 0.5 second time step, and there is a random release of vehicles onto the network. The Q-PARAMICS car-following and lane-changing models are based on a number of other models. Each DVU in the PARAMICS simulation has a target headway. The mean value for this headway is typically around one second, and it varies depending upon the value of certain parameters assigned to the DVU. Lane changing is modelled using a gap acceptance algorithm and a historical record of suitable gap availability. Q-PARAMICS uses unit vectors to describe behaviour at junctions. The vector describes both the point to which a vehicle must head as it exits from a junction, and the required angle of orientation once it gets there. Q-PARAMICS employs an algorithm which defines a general-purpose method to steer a vehicle over a realistic path between its current position to any target position, taking angles of orientation and steering limits into account. The rate of change of bearing is regulated by both the physical attributes of the vehicle and its current speed. Seven predefined vehicle classes are included: cars, light goods vehicles, other goods vehicles (‘1’ and ‘2’, coaches, minibuses and buses, but the user is free to add more vehicle classes as required. Buses follow fixed routes and stop at bus stops (Luk and Tay, 2006).

The Q-PARAMICS software allows traffic modelling to be conducted across the whole spectrum of network sizes, from single junctions up to national networks. The package can handle up to four million links, one million nodes, 32,000 zones and 1 million control points (e.g. stop lines). It is Windows-based, and there is no real limit to what can be modelled within the software. Q-PARAMICS can currently simulate the traffic impact of signals, ramp meters, loop detectors linked to variable speed signs, VMS and CMS signing strategies, in-vehicle network state display devices, and in-vehicle messages advising of network problems and re-routing suggestions.

S-PARAMICS

The latest version of S-PARAMICS is 2005.124. Development of the program has largely been on a project basis. The model includes sophisticated microscopic car-following and lane-changing routines, with dynamic and intelligent routeing, inclusion of intelligent transport systems, and an ability to interface with other common macroscopic data formats and real-time traffic input data sources. It takes full account of public transport and its interaction with other modes at bus stops and through bus priority measures. In addition to its visualised real-time environment, PARAMICS provides very-high-speed batch mode operation for long term statistical studies. Some examples of the visualisation capabilities are given in Figure 3.

Figure 3: Examples of S-PARAMICS visualisation.

S-PARAMICS is currently being applied to trunk, urban, suburban, inter-urban and rural schemes for a very wide range of situations. The extensive list of applications includes signalised roundabouts, bus priority, emissions control scenarios, ramp metering, toll plaza design, area-wide traffic management, road works design, car parks, multi level inter-changes, and complex junction design.

24 http://www.sias.co.uk/sias/s-paramics/paramicsmainpage.html


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2.2.3 DRACULA

DRACULA25 was conceived and developed at the Institute for Transport Studies (ITS) of the University of Leeds (Liu et al. 1995; Liu, 2005a), and is exploited commercially by ITS and WS Atkins consultants. The development, testing and validation of the model were funded by the Engineering and Physical Sciences Research Council, although some early applications of the model were undertaken within the European Commission’s DRIVE II telematics programme.

DRACULA has been used for research in a number of areas, including intelligent speed control, real-time traffic signal control, dynamic route guidance, guided bus operation, bus priority, congestion-based road pricing, and transport demand management. Other potential areas of application range from the operational assessment of temporary traffic management options during maintenance to the design of access roads for major new development sites.

As in VISSIM and PARAMICS, the full DRACULA framework combines a number of sub-models which attempt to represent the behaviour of individual drivers and vehicles in real-time as these evolve from day to day. A demand model represents the day-to-day variability in total demand. For each potential traveller it simulates journey choices such as the route taken and the departure time, based on knowledge of the network, past experience and the perceived network condition. This information is then passed to a traffic simulation model, which represents the within-day variability of network conditions, and simulates the movement of individual (pre-specified) vehicles through the network. Drivers follow their predetermined routes, and en-route they encounter signals, queues and interact with other vehicles on the road. Spatially, the simulation is continuous in that a vehicle can be positioned at any point along a link. At the end of the day, a ‘learning model’ updates the experiences of each individual, and stores the information in their travel history files which, to a greater or lesser extent, influence their next day’s choices (Liu, 2005b).

The are currently there seven types of vehicle defined in DRACULA: ‘dummy vehicles’, ‘small passenger cars’, ‘type 1 buses’, ‘type 2 buses’, ‘taxis, ‘light goods vehicles’ and ‘heavy goods vehicles’. Each vehicle type has a set of individual characteristics, including type, length, desired minimum headway, normal and maximum acceleration, normal and maximum deceleration, desired speed and a gap-acceptance parameter. Vehicles are represented individually, and these characteristics are randomly sampled from normal distributions which are representative of the type of vehicle. Public transport vehicles are represented with additional information such as service number, service frequency, bus stops and average passenger flows at each bus stop. It is unclear why these particular vehicle categories have been selected - for example, why are only ‘small’ passenger cars include? Furthermore, the pre-defined vehicle categories are not sufficiently detailed for use with modern emission models or emission factors. However, users can re-define the vehicle types according to their needs, as long as the correct characteristics are selected. There are nine parameters used in DRACULA to describe the physical and behavioural characteristics of each DVU (Liu, 2005b):

25 http://www.saturnsoftware.co.uk/9.html

• length (m) • minimum safety distance (m) • reaction time (s) • normal acceleration (m s-2) • maximum acceleration (m s-2)

• normal deceleration (m s-2) • maximum deceleration (m s-2) • desired speed factor • gap acceptance factor

Unfortunately, no more than six vehicle-types can be used (the ‘dummy vehicle’ category is reserved for special use and cannot be replaced), and this is insufficient for modern emission modelling purposes, unless a great deal of pre-processing is conducted using an external model. Development is underway to incorporate more vehicle types or user classes into the model.

In the model traffic moves in lanes. A lane can be reserved for a particular type of vehicles only (e.g. a bus lane). Individual vehicle movements are simulated every second, according to car-following, lane-changing and gap-acceptance models. The car-following model calculates vehicle acceleration in response to the desired speed and the relative speed and distance of the preceding vehicle. Depending on the magnitude of the relative distance, a vehicle is classified into one of three regimes: free-moving, following or close-following. The lane-


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changing model contains three steps: (i) obtain the lane-changing desires and define the type of changing, (ii) select the target lane, and (iii) change lane if all gaps are acceptable. Vehicles follow fixed routes from their origins to destinations. Vehicle movements in a network are determined by its desired movement (such as desired speed, lane choice), response to traffic regulations and interactions with neighboring vehicles. The simulation maintains a linked list of vehicles in each lane and moves individual vehicles according to a car-following model and a lane-changing model, and their response to traffic controls at junctions.

The outputs from DRACULA include network-, link- and route-specific values such as total vehicle-hour, total vehicle-km, average travel time, speed, queue length, fuel consumption and pollutant emissions over regular time periods. DRACULA also provides space-time trajectories for individual vehicle, and statistical measures of queue length, flow, flow variation, delay, delay variation, speed and speed variation, and speed distributions. Unlike travel condition measures, which are measured only when vehicles exit a link or exit the network, Emission values are recorded at every second for each vehicle in the network. The program outputs for each link and for the whole network time averages of the pollutant emission and fuel consumption for the current measuring time period (say 5 min interval) and for the whole simulation time period, in the following format and for each link and for the whole network: Each of the output parameters should also be of some use, whether directly or indirectly, for the estimation of vehicle emissions using external emission models. The length of the report time period can be defined by the user. At the user’s request, the program may also output individual vehicles’ second-by-second locations and speeds to provide space-time trajectories of the vehicles. A graphical animation of the vehicles’ movements can also be shown in parallel with the simulation, giving the user a direct view of the traffic conditions on the network (Liu, 2005b).

According to Liu (2005b), the DRACULA emission and fuel consumption models are taken from the QUARTET project (QUARTET, 1992). The relevance of the QUARTET emission functions to current vehicles is questionable, given that the functions are now rather old, although some work has been undertaken using more recent emission models (see Section 2.4.4). DRACULA calculates fuel consumption and emissions (in grammes/second) of CO, NOx and HC for each individual vehicle based on the driving mode (acceleration, deceleration, cruising or idling). For vehicles cruising at a constant speed, the emission factors are defined as a function of speed, and are given for discrete speed points (at every 10 km h-1). Linear interpolation is used to obtain emission factors for speeds between any two discrete points. In other driving modes (idling, accelerating, or decelerating), constant emission factors are assumed. Some values are provided by Liu (2005b), but they are not well explained. For example, the units are not stated, and there is no indication of the vehicle type and technology level to which each value applies. Furthermore, for modern vehicles the use of fixed emission values for all accelerations oversimplifies the situation. The fuel-consumption factors are taken from Ferreira (1982) and the then Department of Transport (1991). Again, these are now out of date. Liu (2005b) states that the models are designed to be flexible, so that new emission rates for the existing pollutants or for new pollutants can be incorporated as they become available, but it is not clear whether this extends to the inclusion of modern emission calculation methods and vehicle classifications which are more complex than those developed in the early 1990s. It is possible for the model to provide information on emissions from individual vehicles, although in the current version only accumulated emission and fuel consumption measures for each link and for the whole network are stored and output at regular time intervals. DRACULA does not model pollution dispersion (Liu, 2005b).

DRACULA is written in C/C++ and runs on a PCs in Windows. The model includes an animated graphical display of vehicle movements in the network. The current release version, entitled DRACULA-MARS (Microscopic Analysis of Road Systems), includes only the traffic simulation model and a simplified ‘departure-time choice model’. This version is designed primarily for existing SATURN users who can combine the SATURN route assignment with DRACULA traffic micro-simulation for detailed network design and/or short-term forecasting. Hence, route assignment is an external input to the model. Within DRACULA-MARS, however, there are functions for choice of departure-time to be modelled, and a wide range of network variability and traffic dynamics (Liu, 2005b).

2.2.4 SISTM

TRL commenced the development of the SISTM (SImulation of Strategies for Traffic on Motorways) software in 1988. It was developed specifically for the assessment of congested traffic conditions on the UK high speed road network, with the aim of developing and evaluation different strategies for reducing congestion. The


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basic software has remained as a research tool, largely limited to the assessment of Highway Agency schemes. SISTM, which is currently distributed as version 5, has been used in the assessment of:

• different motorway layouts (i.e. junction designs), • variable speed limit systems, • ramp metering systems, • modified vehicle characteristics, and • modified driver behaviour,

SISTM is a microscopic motorway simulation, written in FORTRAN, incorporating a car following algorithm that uses a modified Gipps' equation. Driver behaviour is described by two parameters; aggressiveness and awareness, and these are used to produce distributions of desired speed and indirectly desired headway. The time increment used is 5/8th second. Lane changing is controlled through a lane changing stimulus with the user specifying the desire to change lanes. When making a lane changing manoeuvre, a driver is allowed to accept an "unsafe" headway temporarily. This is to allow smooth merging to take place when a driver has to move into a particular lane (SISTM, 2000).

SISTM incorporates the following technical features:

• Network size: 99 km of uni-directional motorway with 9 entry and 9 exit slip roads. 4000 vehicles being modelled at any instant.

• Network details: Motorway geometry to an accuracy of 1 metre. Ghost islands at merges can be modelled. Gradients can be modelled, but bends cannot. Narrow lanes cannot be modelled. Up to 6 main carriageway lanes and 3 slip road lanes.

• Vehicle representation: Up to 8 vehicle types, with different lengths, desired speed, distributions for drivers' acceleration and braking rates.

• Vehicle assignment: No route assignment. User must supply an O/D matrix which specifies the flows from each entry slip road to each exit slip road.

• Control strategies and algorithms: Variable speed limits and ramp metering are all internal to the model.

• User interface: The user can choose to edit text files or use specially written data entry programs. Graphical representation of vehicles as they are being modelled.

During 1999, SISTM was modified to create a prototype version of the software (SISTM 5M), designed for the determination of the operational parameters for MIDAS (Motorway incident detection and automatic signalling) (Babb and Baguley, 1998; Abbott et. al., 2000). During this process a number of additional features were added to SISTM functionality, including a module to estimate vehicle emissions and fuel consumption. In addition, SISTM was extended to include the calculation of average traffic noise. Whilst the outputs for noise are not suitable for the purpose of assessing noise nuisance, they do provide some indication of potential mitigation when comparing different modelling scenarios (Abbott et. al., 2000). In addition, this version of SISTM was further enhanced with the inclusion of an assessment tool for driver stress.

SISTM has been subject to on-going validation studies, routinely involving its application across samples of high speed road networks, combined with an in-depth evaluation on the M25 junctions 10 – 16 variable speed limit pilot regime.

Finally, the SISTM software was developed with improved output formats. A screen dump of the enhanced version of the main simulation program, SIMRUN 5M, is shown in Figure 4. Whilst the latest versions of SISTM have an improved visualisation, it remains less developed than those of VISSIM and PARAMICS.


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Figure 4: A screen dump example of the SISTM SIMRUN visualisation (Abbott et. al., 2000). 2.2.5 Summary It is clear from the above discussion that all four traffic micro-simulation models are very detailed. According to Nam et al. (2003), the commercially available models have only minor differences, and all reproduce the fundamental traffic flow-density relationships well. Driver behavior models can differ in detail, but all are based on car-following and lane changing algorithms reported in the literature. Since the algorithms are required to be stable and computationally efficient, there are limitations on how driver behavior can be quantified. Some of these limitations can have significant effects when emissions models are integrated with the traffic models.

A comparison between the two most widely available (commercial) models, VISSIM and S-PARAMICS undertaken by TfL (2003) showed that the packages were generally very similar, with both requiring large amounts of input data. PARAMICS is more suited to bigger networks and motorways while VISSIM is more suited for detailed urban driving conditions. The aspects which were compared, and the subjective assessments, are summarised in Table 3. It is worth noting that the pollution modelling capabilities of S-PARAMICS were considered to be more advanced than those of VISSIM, although more precise information was not provided.

Table 3: Comparison between VISSIM and S-PARAMICS (TfL, 2003).

Aspect VISSIM S-PARAMICS

Data input Quicker Network changes Easier Calibration Quicker Validation Similar Option testing Quicker Animation Better Pollution modelling More advanced Pedestrian modelling Better Output More friendly format Literature availability Published Not published


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2.3 Examples of model integration

As observed earlier in this Chapter, some micro-simulation traffic models already contain embedded emission functions. However, there have been several studies in which a micro-simulation traffic model has been combined with an external instantaneous emission model, and these studies are reviewed in this Section of the Report. Such ‘integrated’ models have subsequently been used to test a number of traffic-related scenarios. However, the emphasis here is on the mechanics of model integration, rather that the results of model application.

2.3.1 VISSIM-MODEM

Park et al. (2001) linked version 2.30 of VISSIM to the original version of MODEM, and the resulting model was applied using real traffic data in Maidstone, Kent. The results were compared with measured pollutant concentrations and with the standard UK procedure for air quality assessment, as outlined in the Design Manual for Roads and Bridges (DMRB). Little detailed information was provided by the authors in relation to the interface between VISSIM and MODEM. The latter can be used to predict emissions from petrol and diesel cars, based on a driving pattern in which vehicle speed is described as a function of time. The instantaneous vehicle position and vehicle speed outputs from VISSIM were therefore used as direct inputs to MODEM (it is assumed that this was conducted separately), and emissions were calculated for each road link. MODEM does not provide emission estimates for post-Euro I cars, light commercial vehicles and heavy-duty vehicles. No explanation was provided of how emission from these vehicles were estimated. For the VISSIM component of the model, the types of HGV entering the network are defined by vehicle length (distribution between 8m and 12m), whilst passenger vehicles are modelled using a constant length of 4.5m. Again, no information is provided by the authors in relation to how cars were classified in a more detailed manner for use in MODEM.

2.3.2 VISSIM-PHEM

During 2005, RPS Group on behalf of the HA, trailed a dynamic simulation modelling exercise for traffic emissions on the M1 north-east of Sheffield (Smyth 2004). The trial was run to assess the effects of the SWYMBUS26 scheme within the M1 Air Quality Management Area north-east of Sheffield. The assessment procedure was undertaken using peak hour traffic data, to generate input suitable for predictive air dispersion modelling (using INDIC AIRVIRO) of improvements to the M1, between Junctions 33 and 35, as proposed under the SWYMBUS study. The prediction of vehicular emissions were undertaken using the VISSIM/EnvPro traffic micro-simulation modelling software, coupled with the MODEM/PHEM exhaust emission models. With in excess of 100,000 vehicles a day, the M1 motorway remains the most significant contributor to NO2 concentrations in the study area. Modifications to the M1, including widening and a speed limit reduction, are likely to change traffic flow characteristics and traffic volumes, with a knock-on effect on local air quality. This study provided a quantitative assessment of the changes in vehicular emissions, resulting from changes in road traffic due to the proposals, as predicted by the VISSIM/PHEM/MODEM (VMP) approach. For the purposes of comparison and validation, emission rates from the National Atmospheric Emissions Inventory (NAEI) database (based on 2002 NAEI emission factors) and the DMRB, which would have been the standard way of modelling vehicular emissions for a study of this type, were also derived and compared with the outputs from VMP.

2.3.3 VISSIM-CMEM

In a study by Nam et al. (2003), VISSIM 3.60 was integrated with the load-based Comprehensive Modal Emissions Model (CMEM, Preliminary Draft Version 2). CMEM, which is based on the principle that the engine load dictates the emissions (Barth et al., 2001a), was summarised in the Task 1 Report (Boulter et al. (2006). The emissions and travel times associated with varying levels of driver ‘aggressiveness’ (defined as

26 SWYMBUS (South West Yorkshire Making Better Use Study). Review of the VISSIM/MODEM/PHEM approach for generating peak hour vehicular emission for M1 junctions 33-35.


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the root mean square of a power factor) were measured using an instrumented vehicle on a road network in southeast Michigan. The emissions model was calibrated with data acquired during the laboratory tests on the instrumented vehicle. Model calibration entailed adjusting the parameters to produce optimal fits with the data, and was conducted using second-by-second dynamometer data. Only hot exhaust emissions were considered. The modelled emissions were within 30% of measured values (Nam et al., 2003). A ‘virtual’ vehicle, having similar characteristics to the instrumented vehicle, was then simulated in VISSIM. The southeast Michigan road network was coded into the traffic model. The generated driving trace of a simulated vehicle was found to be quite different from the measured traces. However, the aggregate measurements, such as travel time and aggressivity, compared well with measured values.

The only modifications made to CMEM by Nam et al. (2003) were in the engine-out NOx module, in order to incorporate air conditioning effects. During a simulation information on vehicle speed, acceleration, type and weight, in addition to the road gradient, was iteratively passed from VISSIM to CMEM. The code for VISSIM is proprietary, and therefore a special software interface was provided by PTV to link the two models. The interface returned calculated emissions and fuel values to VISSIM, which recorded the results in a file. Where the emissions were calculated for many vehicles simultaneously, the run time of the traffic model increased significantly. According to Nam et al. (2003), the driving patterns recorded in the real world could easily be obtained in 'real-time' directly from VISSIM, and the emission modelling results would be identical.

The model was also used to quantify the relationship between emissions and the aggressiveness of driving. It was found that aggressive driving naturally caused significantly higher emissions, and an ‘aggressivity number’ may be employed as an explanatory variable for emissions in future studies, as well as a means for comparing drive cycles. This concept is similar to that of ‘driving dynamics’ which has been applied in Europe for some time.

In a UK study, Stathopoulos and Noland (2003) and Noland and Quddus (2006) also combined the VISSIM with the CMEM database, and further work on the model linkage was reported by Chevallier (2005). Figure 5 summarises how VISSIM and CMEM were integrated. The vehicle operating variables generated from VISSIM (vehicle identification, vehicle type, second-by-second speed and acceleration) were adjusted for input into the batch mode of operation of CMEM. The C++ program created in previous studies (see above) was used to convert the output VISSIM file into the required input files for the CMEM model. Road gradient was assumed to be equal to 0% and specific humidity was set to 80 grains of water per pound of dry air and kept constant throughout the simulation (NOx emissions are influenced by humidity, which is taken into account in CMEM).

One of the goals was to adapt the emission data obtained from the 1997 Californian vehicle fleet (in CMEM) to the 2005 UK fleet. The CMEM categories are based on vehicle type (car/truck), emission status (normal/high emitters), fuel and emission control technology (US standards), power-to-weight ratio and accumulated mileage. For light-duty vehicles the EU and US emission categories were aligned to determine the proportion of vehicles in each of the different technology classes. The assumed correspondence between EU and US car emission standards is shown in Table 4. Chevallier (2005) equated the oldest vehicles in CMEM - corresponding to ‘no catalyst’ or ‘2-way catalyst’ - to pre-Euro I vehicles in the EU, and ‘3-way catalyst’ vehicles to Euro I. It is assumed that this relates to petrol vehicles, and no information is provided on the separate treatment of diesel vehicles. Five CMEM categories correspond to Tier 1 standards, and these are differentiated according to the accumulated mileage of vehicles. In order to determine the number of UK vehicles in each category, it was assumed that the number of miles travelled was related to the vehicle age. It was assumed that the proportion of high-emitting cars was 5%, with each of the five types of failure used in CMEM affecting 1% of the fleet.


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Figure 5: The linkage of VISSIM and CMEM (Chevallier, 2005).

Table 4: Assumed correspondence between EU and US car emission standards (Chevallier, 2005).

EU legislation US legislation

Pre-Euro I No catalyst or oxidation catalyst Euro I Three-way catalyst Euro II Tier 1 Euro III Tier 2 Euro IV Tier 3

Noland and Quddus (2006) used the integrated VISSIM-CMEM model to examines whether road schemes that increase the availability of road space or which smooth the flow of traffic result in increased vehicle pollution. Increased traffic was found to quickly diminish any initial emission reduction benefits.


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2.3.4 PARAMICS-CMEM

The integration of CMEM with PARAMICS was undertaken within the California PATH27 programme (Malcolm et al., 2001; Barth et al, 2001b). The integrated modeling tool was validated with real-world traffic and emissions data from existing tunnel studies. The average predicted speeds and NOx emissions were slightly higher than the field measurements.

PARAMICS has an open architecture which allows a user to create different plug-in modules for a variety of functions. This is achieved through the use of APIs. Integrating CMEM within PARAMICS was accomplished by creating an API through the use of the PARAMICS Programmer utility. Programmer is a framework that allows the user to access PARAMICS features and variables as the simulation takes place. The CMEM/PARAMICS API was written in C and contained two elements: (i) control functions and (ii) callback functions. The control functions are functions that PARAMICS uses as part of its standard simulation. These allow the user to over-ride or add additional code to the simulation. The callback functions allow the user to retrieve specific information from the simulation, such as vehicle and network attributes. The CMEM API for PARAMICS calls the CMEM function during the PARAMICS simulation in order to obtain emission values for each vehicle at each second. Currently the CMEM API summarises link emissions for every 15 minutes of simulation (Barth et al, 2001b).

When using the integrated PARAMICS /CMEM model, 26 different vehicle types should be defined within PARAMICS to correspond to the categories in CMEM. The fleet percentages of these categories can then be directly applied within PARAMICS. Each of the 26 vehicle categories has the same performance characteristics. This is not true in real life, since some of the vehicle categories will have more powerful performance characteristics than other categories, and the determination of more appropriate vehicle performance characteristics for the 26 vehicle categories of the CMEM model is an area of future research (Barth et al, 2001b).

2.3.5 PARAMICS-MODEM

In the summer of 1995, a variable speed limit (VSL) pilot scheme was introduced onto part of the M25 Motorway. The purpose of this scheme was to try to reduce the stop/start driving and traffic ‘bunching’ that sometimes occurred during periods of heavy congestion. By reducing the legal speed limits to either 97 km/h (60 mph) or 80 km/h (50 mph), dependant on the amount of congestion, it was hoped that slowing the maximum speed would even out the traffic flow resulting in steady speeds being maintained and improving traffic flow. By changing traffic speeds it was also believed that this would have some effect on the exhaust emissions (Barlow, 1999).

Two approaches were taken to evaluate the effect of the VSL upon emissions. Firstly an instrumented car was employed, designed to record typical vehicle speed data before and after the implementation of the scheme. The second approach was undertaken by PARAMICS, and involved the use of traffic simulation model, specially developed for the VSL scheme. Both approaches incorporated the use of the extended MODEM model (See section 2.3.7).

During 1995 and 1996, instrumented vehicles were driven over the scheme domain, before and after the implementation of the VSLs. To provide a driving cycle that represented driving condition on the motorway, the logged data were analysed, sections extracted and compiled to produce a complete cycle whose characteristics were similar to the averages of all the logged trips. This produced a driving cycle with a duration of approximately 60 minutes, and a maximum speed of 145 km/h. A typical example of the derived emission distribution is shown in Figure 5, plotted as a surface plot. This shows relatively low emission at low speed and low accelerations. At high speed and under high acceleration, the emissions rates are much higher.

27 PATH = Partners for Advanced Transit and Highways


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0

25

50

75

100

125150

Speed(km/hr)

-40-30

-20-10

0

10

20

30

Speed * Accel(m2/s3)

020040060080010001200

1400

1600

0200400600

800

1000

1200

1400

1600

CO Emissions (mg/s)

CO Emissions (mg/s)

Figure 5: A typical emission distribution (Barlow, 1999).

Quadstone were commissioned to investigate the effects of the VSL scheme upon emissions through the use of the PARAMICS traffic simulation model. This model was originally designed to simulate the variable speed limit scheme at an individual vehicle level to provide information upon traffic flow, transit times and congestion. In order to investigate the effect of the VSL scheme upon emissions, the following two scenarios were simulated by the model: No speed control operation. Speed control operation. To simulate these scenarios, the model required a number of parameters to be incorporated within it. These are grouped as follows: Traffic network Table 5 lists the parameters that were used to describe the traffic network in the model simulations. The values that were assigned to these parameters for the emissions study corresponded to the values in the original model.

Table 5: Traffic network parameters. Parameter Description Nodes Co-ordinates of junctions Links Description of all links, ramps and slip

roads Arcs Centres and radii of arc links Kerbs Positions of the ends of edges of links Categories Speeds, widths, number of lanes on

different road types Detectors Positions of vehicle detection loops Beacons Position of variable speed control signs Priorities Junction priorities (on approach roads to

motorway) Zones The zone areas as specified by the traffic

demand


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Vehicle Parameters The individual vehicles are termed Driver Vehicle Units (DVU's) and were represented by the set of parameters listed in Table 6. The dynamic parameters listed were updated at each time step of the simulation, the latter being a user-defined variable that can range from 1 minute to 1 hour.

Table 6: DVU parameters.

DVU Parameter Type

Static

Type (further defined by set of physical parameters) and age of vehicle.

Origin and destination of vehicle.

Aggressiveness and awareness of driver.

Dynamic

Position (lane and distance from stop line), speed and acceleration.

Next turn intention. Target lane range, target lane on next link.

Exit index (on roundabout).

Time stopped on journey and link.

Time travel on journey and link. Traffic demand The traffic demand for the network was simulated by a matrix of origin/destination movements, with elements representing the actual number of trips between defined specific origins and destinations during a defined time period. These trips were loaded on to the network in zones that are associated to the links. During the simulation, vehicles were released from the origin zone and made their way towards the destination zone, with limited knowledge of the network and their immediate environment. The demand data used in this study dated from 1994 and was that used in the original model. Other data inputs To assess the total emissions of pollutants from the simulations, the extended MODEM data were incorporated into the model. A vehicle's rate of pollution was calculated at each simulation time step and added to pollution totals for the link that it was traversing. The collection of pollution counts was made at a user-defined frequency and the current pollution counts were recorded to a file for each link.

The algorithm for defining the setting of the speed limits was also required. The speed limits in the VSL scheme correspond to a series of Congestion Alert Levels (CAL) and are dependent upon the speed and flow characteristics of the traffic. The speed and flow characteristics are each defined by their own alert thresholds. The speed alert thresholds are defined in 6 speed bandings, which changes in response to the speed change at a specified location, this corresponding to the loop detectors embedded in the road surface. The flow alert thresholds are defined in three bandings, which similarly to the speed alert, is changed by a change in flow. Simulations Six simulations were run in total, three with and three without the variable speed limits in operation. The simulations were run for 24 hours and generated a pollution file once every 15 minutes. The pollution data for the northbound and southbound carriageways were stored in separate matrices throughout the simulation, allowing their results to be compared.


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2.3.6 DRACULA-CMEM

Tate et al. (2005) describe the integration of DRACULA and CMEM. Some of the shortcomings of the DRACULA model were addressed in this work. For example, DRACULA was extended to include several new vehicle categories. New light-duty vehicle categories were created for the CMEM (US) vehicles that were considered to be representative of the UK fleet. The integrated model was applied in the city of York.

The default parameters used in DRACULA to categorise vehicles (see section 2.2.3) were adjusted according to the data recorded using an instrumented vehicle in York. In particular, the maximum acceleration rate for light-duty vehicles was changed to reflect the measured values, and a lower normal acceleration rate was introduced. The EU and US emission categories were aligned to determine the proportions of the different technology classes of light-duty vehicles. Medium-size commercial vehicles were represented by CMEM category 17 (Tier 1, GVW 3751-8500 kg). The heavy goods vehicles and busses in the study area were assumed to be CMEM category 40 (diesel, >8500 kg). No data were available to specify the proportions and types of high-emitting vehicles. CMEM was verified against second-by-second emission measurements (Euro III petrol car) to assess the validity of using a US-based model and vehicle fleet characteristics in the UK. It was concluded that emissions from Euro III vehicles (only CO was reported) are lower than those of US Tier 1 vehicles. There is clearly therefore considerable uncertainty in applying CMEM in the UK. Comparisons with the emission predictions from the built-in DRACULA functions were not reported.

Since the original release of CMEM (reviewed in Boulter et. al., 2006), the model has been extended to include HDV emission classes, the inclusion of portable emissions measurement system (PEMS) data, and the inclusion of three new light-duty vehicle categories: LEV (Low Emitting Vehicle), ULEV (Ultra Low Emitting Vehicle), and SULEV (Super Ultra Low Emitting Vehicle), based on the California Air Resources Board’s certification standards. In 2005, a particulate matter module for CMEM was commenced (Scora and Barth, 2006).

2.3.7 SISTM-MODEM

In the early 1990’s, INRETS (France), TÜV Rheinland (Germany) and TRL jointly developed the MODEM emissions model as part of the EU DRIVE project MODEM (Jost et al 1992). The resulting functions allowed the estimation of individual vehicle emissions and fuel consumption from second-by-second vehicle speed traces. However, the original DRIVE project focussed on urban conditions, and thus in 1997, TRL produced an extended version of MODEM for motorway, high speed conditions (Barlow, 1997). Within this study instrumented cars were driven through the M25 controlled motorway section (junctions 10-15) to record driving typical characteristic. These data wee subsequently analysed to develop a series of test cycles which wee subsequently used in conjunction with a chassis dynamometer to record corresponding emissions.

The resulting data for each vehicle type were expressed as a series of tables, one for each pollutant (CO, CO2, HC, NOX and in the case of diesel vehicles, PM) in milligrammes per second, and one for fuel consumption in millilitres per second. The tables contained entries for speeds (to the nearest km/h) in the range 0 to 153 km/h and speed and, acceleration values (to the nearest m2/s3) in the range -40 to +30 m2/s3. Vehicles were then grouped by engine size and fuel type, and weighted averages calculated to produce a single table for each emission substance in each of 8 vehicle class categories. Empty cells were filled by extrapolation, and the resulting data smoothed. Validation of the emission functions was performed by comparing the actual measured emission quantities over the test cycle with the estimates produced by the derived functions. The level of agreement was “very good” (Barlow, 1997). A full description of the SISTM emission sub models is available from Abbott et. al., 2000.

In 2000, instantaneous emission functions for motorway vehicles other than cars were not readily available. Relatively little data existed on the exhaust emission rates for vehicles other than cars in any form, and this was thus this was one of the main rationales for the TRAMAQ project UG216, which characterised the emissions from a sample of approximately 50 heavy goods vehicles and buses (Latham et. al., 2005), and the EU ARTEMIS programme. Therefore, average speed HGV emission factors were used within SISTM, derived from the EU 4th Framework MEET project, TRL data and supplemented by data from the Swiss-German-Austria handbook of emission factors (INFRAS, 1995). Although these functions did not permit the calculation of emission rates using instantaneous speed data, nor were they solely derived from motorway journeys, they did support the calculation of emissions for individual vehicles based on mean vehicle speeds.


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The MEET-SISTM HGV functions provided estimated exhaust emission rates for CO, CO2, HC, NOX and PM. Supplementary functions were available that provided correction factors for the effects of travelling on roads with a gradient, and for the effect of different levels of vehicle loading.

SISTM incorporates the carbon balance method for the estimation of fuel consumption. Under this method, it is assumed that all of the carbon in the fuel is converted into four of the exhaust emission substances, CO, CO2, HC, and PM, and consequently that the same mass of carbon is present in both the fuel and the resulting exhaust emission substances (Hickman 1998).

2.3.8 Other examples

The aim of the DIANA (Development of integrated air pollution modeling systems for urban planning) project was to create a comprehensive traffic and emissions modeling system. In the project, the traffic micro-simulation model HUTSIM and its emission calculation sub-program, both developed by the Helsinki University of Technology (Kosonen, 1999), were integrated with an atmospheric street dispersion model developed by the Finnish Meteorological Institute. The emission sub-module calculates fuel consumption and emissions using speed-acceleration look-up tables.

Barth (1998) proposed an application of CMEM known as ITEM (Integrated Transportation/Emissions Model). ITEM was a modeling framework designed to integrate CMEM with a hybrid macroscopic/ microscopic traffic model. By combining a macroscopic traffic assignment model with a set of microscopic simulation models (organized by road type), both regional and local emission inventories could be estimated. The primary component of ITEM was a macroscopic traffic assignment model that can dynamically determine link densities and speeds for a regional network. This component provides input into microscopic simulation sub-models that incorporate detailed emissions data for the particular case they simulate.

TRANSIMS (TRansportation ANalysis and SIMulation System), developed by Los Alamos National Laboratory, is an integrated system of models (Los Alamos National Laboratory, 2002 - cited in Capiello, 2002). The model has been implemented in the city of Portland, Oregon. TRANSIMS includes modules which deal with population, household and commercial activity, route planning, traffic simulation and emissions, and also features a visualisation tool. In the traffic simulation module, the road network split into 7.5 m cells. The model can be viewed as a hybrid between a mesoscopic and a microscopic model. The speed is defined in 16 mph ranges. Since the emission module requires a finer resolution, the vehicle trajectories are smoothed using frequency distributions of the product of speed and acceleration. This results in speed and acceleration values which are clustered in bins. The emission module has three sub-modules: an evaporative module, a light-duty exhaust module, and a heavy-duty exhaust module. The light-duty exhaust module uses CMEM to populate emission maps with bins of 2 mph for speed, and 1.5 ft s-2 for acceleration. Exhaust emissions of CO2, CO, HC , and NOx, and fuel consumption on 30 m road segments are calculated using the traffic model, the distribution of accelerations, and the emission maps. The emission module can be combined with the EPA’s MODELS-3 to produce three-dimensional hourly gridded emissions over the metropolitan area.

2.4 Comparison of model parameters for UK conditions

In this Section, the output parameters from micro-simulation traffic models are compared with the input parameters required for emission modelling in the UK. The emission models considered are MODEM, CMEM and PHEM, and the traffic models are VISSIM, PARAMICS and DRACULA. Traffic–related emission sources were briefly summarised in the introduction. Instantaneous emission models deal principally with hot exhaust emissions. Other sources, which are not normally modelled on an instantaneous basis and were therefore not discussed in detail, include cold-start exhaust emissions, evaporative emissions and the generation of particles via non-exhaust processes such as tyre wear and brake wear. The most important factors in relation to the estimation of vehicle emissions can be summarised as the road characteristics, the traffic flow, the vehicle operation and the vehicle classification. These are discussed briefly below.

2.4.1 Road characteristics

For some emission models a description is required of the road traffic characteristics. For example, the area


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type (urban or rural) and the speed limit are inputs to traffic situation models such as the Handbook of Emission Factors (INFRAS, 2004). Such parameters are not always defined in micro-simulation traffic models, but this is not a major drawback as far as this project is concerned, as the parameters are not generally required for micro-simulation emission modelling, and in any case they can generally be otherwise obtained by the model user. The road segment length is an important parameter in any model which is used for estimating emissions, and this is included in all traffic models. One of the most important factors is the road gradient, which has a large effect on emissions from heavy-duty vehicles, and should be taken into consideration. Table 7 compares road characteristics parameters in the emission and traffic models.

Table 7: Road characteristics parameters.

Parameter Units Emission model input Traffic model output

MODEM CMEM PHEM VISSIM PARAMICS DRACULA

Road segment length m Road gradient %

2.4.2 Traffic flow

Emission factors for different types of vehicle are normally presented in terms of grammes per vehicle-kilometre. These then need to be weighted by the number of vehicles travelling on a section of road during a given period. For emission modelling purposes, only the total number of vehicles per specified period is required. As this type of information is fundamental to micro-simulation , this aspect of model integration is unlikely to present any difficulties. Traffic density is also used in the traffic situation modelling approach to define vehicle operation, but this parameter is not needed in instantaneous emission models (Table 8) as the driving pattern must be specified anyway.

Table 8: Traffic flow parameters.

Parameter Units Emission model input Traffic model output


Flow per user-defined period veh h-1

2.4.3 Vehicle operation

The subject of vehicle operation is one of the most complex aspects of emission modelling, and its treatment in various emission models was covered in the Task 1 Report (Boulter et al., 2006). Average speed has traditionally been used to predict emissions. However, for micro-scale modelling average speed is a poor indicator of the emission level. For the sake of emissions accuracy it is crucial to take into account the kinematics (the driving pattern) and the power demand. Another important parameter, especially for HDVs, is the vehicle load. Vehicle operation is one of the crucial considerations when integrating traffic and emission models. In the case of micro-scale modelling, there is a reasonably close correspondence between the outputs of the traffic models (speed as a function of time and location) and the input requirements of emission models (Table 9). However, information on vehicle loads will need to be obtained from sources other than the traffic models.

Table 9: Vehicle operation parameters.

Parameter Units Required as emission model input Produced as traffic model output


Driving pattern (speed = f(t)) km h-1 Vehicle load (LGVs, HGVs, buses) % a


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a Optional.

2.4.4 Vehicle classification

One of the main challenges in micro-scale model integration is the matching of the different vehicle types defined in the traffic simulation and emissions components. The systems of vehicle classification required for emission models are generally more complex than those used in traffic models. In emission models, hundreds of different vehicle categories are required to take account of the various factors affecting emissions. Traffic models typically have different vehicle types that are based on how they operate within a roadway network. In addition to the obvious divisions of vehicle types (i.e. motorcycles, passenger cars, buses, HGVs), categories are often made based on vehicle performance (e.g. high-performance cars, low-performance cars) that can be closely related to traffic simulation parameters. In the case of HGVs, transportation models typically categorise vehicles based on their configuration and number of axles. In all cases, a straightforward approach to handling the vehicle matching is to create an appropriate mapping between the vehicle types defined in the traffic simulation model, and the vehicle types defined in the emission model (Barth et al, 2001b). From an emissions perspective, vehicles can be therefore separated into ‘coarse’ categories (e.g. cars, LCVs, buses, HGVs, motorcycles), for which it might be possible to obtain correspondence with traffic models, and ‘detailed’ categories (e.g. categories of emission legislation, weight, engine capacity) which relate to emission legislation and would not be expected outputs from traffic models. For the European emission models – MODEM and PHEM - the coarse and detailed vehicle categories are listed in Table 10 and Table 11. Neither MODEM nor PHEM cover direct-injection petrol cars, cars using fuels other than petrol or diesel, light commercial vehicles (M, N1), or motorcycles.

Table 10: ‘Coarse’ vehicle classes.

Emission model Vehicle class Fuel Size

MODEM PHEM Cars Petrol All Diesel All Petrol DI All CNG All LPG All Hybrid All LCV Petrol M+N1-I M+N1-II M+N1-III

Diesel M+N1-I M+N1-II M+N1-III

CNG/petrol All HGV Diesel Rigid, <7.5t Rigid, 7.5-12t Rigid, >12-14t Rigid, >14-20t Rigid, >20-26t Rigid, >26-28t Rigid, >28-32t Rigid, >32t Artic, <=7.5t Artic, >7.5-14t Artic, >14-20t Artic, >20-28t Artic, >28-34t Artic, >34-40t Artic, >40-50t


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Artic, >50-60t Petrol Rigid, all CNG Rigid, all Urban bus Diesel <15 t,15-18 t, >18 t CNG <15 t,15-18 t, >18 t Ethanol All <15 t,15-18 t, >18 t Coach Diesel All <15t/15-18/>18 t CNG All <15t/15-18/>18 t Motorcycles Petrol All

Table 11: ‘Detailed’ vehicle classes.

Emission model Vehicle class Fuel Emission

legislation

MODEM PHEM Petrol ECE 15.04 All pre-Euro I Euro I Euro II Euro III Euro IV Euro V Petrol, DI All Diesel All pre-Euro I Euro I Euro II Euro III Euro III + PM trapa Euro IV Euro IV + PM trap

Cars

Euro V Petrol All LCVs

Diesel All Diesel All pre-Euro I Euro I Euro II Euro III Euro IV Euro V CNG Euro II Euro III

HGVs

Euro IV Diesel All pre-Euro I Euro I Euro II Euro III Euro IV Euro V CNG Euro II Euro III

Urban buses / coaches

Euro EEV Motorcycles Petrol All


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In CMEM the categories are based on vehicle types and emission standards which are relevant to the US. For example, CMEM has 26 different categories of light-duty vehicle. The basic car categories are ‘no catalyst’, ‘2-way (oxidation) catalyst’, ‘three-way catalyst’, and ‘Tier 1’. There are also specific categories which relate to high-emitting vehicles. In order to use CMEM in Europe, a correspondence between the US emission standards and EU emission standards must therefore be established, a process which is not straightforward for a number of reasons. For example, the US and EU emission standards have different values, were introduced in different years and based on different test cycles. Even where a correspondence can be obtained between the US and EU emission standards, there is no guarantee that real-world (off-cycle) emissions will be equivalent for the corresponding US and EU vehicles, given the different vehicle design standards, possible differences in the operation of fuelling and emission control systems, and differences in vehicle operation. Furthermore, it should be noted that in the US standards a system of ranges, and the emission limit value refers to an average value for each manufacturer. This implies that a car manufacturer can produce models with quite different emission characteristics. For these reasons, the use of CMEM in Europe cannot be recommended.


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3 Links between emission and air pollution models

3.1 Types of air pollution model

Air pollution or ‘dispersion’ models are now widely used for predicting past, present and future concentrations of pollutants, as well as short-term temporal and spatial variations. Such models can provide useful insights into the physical and chemical processes that govern the creation, dispersion and transformation of pollutants. For example, the contribution of different source sectors to ambient pollutant levels can be deduced from model calculations.

A wide range of different models has been published in the scientific literature. Models can be categorised in a number of different way, including the physical concepts involved, the temporal scale and the spatial scale. A distinction can also be drawn between process-oriented models and statistical models. The former are based on the descriptions of physical and chemical processes (cause and effect), whereas the latter are based upon the interpolation and extrapolation of measured data.

A review of some of the models used for ambient NO2 and PM10 have been provided by AQEG (2004 and 2005) and in standard text by Pasquill and Smith (1983). Four types of modelling approaches have been routinely employed: Gaussian The Gaussian formulation describes the transport and diffusion of a material from a source to a receptor according to stability class and other atmospheric characteristics, such as wind speed, and horizontal and vertical standard deviations of the plume. Early applications of Gaussian models were rather simple, usually being used to describe the long-term behaviour of an elevated release from a stack. However, Gaussian models have become quite sophisticated (e.g. ADMS) and have accommodated much more detailed descriptions of dispersion from elevated sources, line sources and volume sources, and can incorporate chemistry (e.g. NO2-O3 titration). It has been routinely applied to plumes from line sources, characteristic of road transport. Lagrangian A Lagrangian model is often taken to refer to a moving coordinate system and can take the form of a trajectory box model or a particle model. Such models are sometimes referred to as random walk models. Lagrangian models can be quite computationally intensive, and can accommodate chemistry sub-models. Eulerian In contrast to the moving Lagrangian framework, Eulerian models operate on a fixed coordinate system, where the emission, transformation and transport are parameterized in terms of the fluxes to and from a two or three-dimensional fixed coordinate system. Such a modelling system tends to be mathematically complex, and have not been widely applied to road transport sources. Empirical and semi-empirical models Empirical models were originally developed as an aid to mapping air pollution concentrations in a more rigorous manner than could be accomplished with sparse measurements. The basis is to take the sparse measurements and correlate them with some reasonable proxy statistic for the emission source density (or the emissions themselves) to determine a relationship between this proxy and ambient concentrations. It is assumed that the proxy data are much better known or are available on a much finer scale such that the relationships can be used as the basis of mapping pollutant concentrations. As such, this technique works best with relatively short-lived pollutants, such as NOX, or where the emission source density dominates the ambient concentrations. The advantage over interpolation of sparse measurements is great where robust relationships can be shown from independent data. Moreover, the computational requirements of this technique are reasonably low.


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Another way of classifying models is as follows:

• Point source dispersion models used for simple industrial sources. • Line source dispersion models used for airport and roadway air dispersion modelling. • Area source dispersion models used for forest fires or dust storms. • Photochemical models used to analyse reactive pollutants. In relation to transport, the second and fourth models are the most relevant. However, in urban areas, dispersion is affected by atmospheric flow changes produced by building street geometry and aerodynamic effects. Microscale air pollution simulations are a complex task since the time scales are comparable to the spatial scales. Initial and boundary conditions are also vital. Microscale Computational Fluid Dynamics (CFD) models are playing an increasing role in air quality impact studies. CFD modelling is based on the numerical solution of the fluid flow and dispersion equations, which are derived from basic conservation and transport principles: (i) the mass conservation (continuity) equation, (ii) the three momentum conservation (Navier-Stokes) equations in three dimensions, and (iii) the transport equation for pollutant concentration. CFD models are computationally demanding and therefore mainly used in research applications. Popular CFD codes include PHOENICS, FLUENT and STAR-CD) (Vardoulakis et al., 2003; Vardoulakis et al., 2005, Ning et al., 2005).

Whilst there have been significant developments within the integration of meteorological wind field data into dispersion models, all of the commercially available air pollution models appear to be restricted to the use of road transport, average speed emission factors.

3.2 Emission and dispersion model integration

All dispersion models require vehicle emission factors as input, although some operational models might include default national emission factors. It is generally recognised that emission factors represent one of the most important source of uncertainty in modelling traffic pollution (e.g. Kühlwein and Friedrich, 2000).

Emission models generate emission factors - for specific pollutants, specific vehicle types and specific operational conditions – which are stated in mass per unit distance (e.g. g km-1). The emission rate of the traffic is calculated by multiplying the emission factor for each vehicle category by the numbers of vehicles in that passing along a section of road per unit time, and summating over all vehicle categories. This gives an emission rate for the whole traffic in mass per unit distance per unit time (e.g. g km-1 h-1)

Instantaneous emission models can be used to predict both cycle average emissions and emissions on a continuous basis (normally every second of a driving cycle). Cycle average emissions - normally derived using average speed models - are currently used in most air pollution prediction models. However, with emission factors of this type it is not possible to ‘map’ emissions with a high spatial resolution.

One potential benefit of instantaneous models is that they allow emissions to be resolved with greater spatial detail, and thus have the potential to lead to improvements in the spatial prediction of air pollution. For this to be possible, there is a need to understand the links between the outputs of instantaneous emission models and the input requirements of the detailed types of air pollution model which permit mapping. Such models typically involve the use of computational fluid dynamics (CFD), and examples include MIMO (Ehrhard et al., 2000) and ANDREA. With respect to CFD model operation for finer spatial and temporal resolution calculations, there would of course be longer calculation times required. Also there should be boundary conditions given with the required resolution accuracy otherwise the model would just interpolate on the boundary thus giving "accurate" results within but only based on interpolation approximations. Furthermore since the mesh would be finer, one could alter the CFD code and change the order of accuracy in calculating the inviscid28 terms (e.g from 3rd to 2nd) or likewise for the unsteady terms in order to save computational time without seriously affecting the accuracy of the results.

28 A flow in which viscous effects can be neglected is known as inviscid flow


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3.2.1 Models

Airviro and ADMS are two of the most widely used air pollution modelling suites in the UK. AirViro Airviro 3.0 is a completely Web-based environmental GIS tool. It has been widely applied to urban applications and there are currently more than 80 Indic-Airviro installations, used as part of environmental management applications. It is a modular system and therefore any specific user can start with limited functionality and develop the systems functionality over time.

Indic-Airviro's main modules are:

• Indico - Data collection and time series analysis including presentation of data in real time.

• Indico validation - Supports validation of measured data.

• EDB - An emission database that describes emissions to air and water in an area with many sources emitting several substances with different time variation.

• Dispersion - A module to run Dispersion calculations and to display and analyse the result. Currently 6 different models are supported including the Airviro grid and Gaussian models as well as a street canyon model. The US EPA AERMOD model is supported.

• APUB - Publication of environmental data for the public on the internet The Airviro server runs on a normal PC under a Linux operating system. This gives the optimal combination of stability and price. Airviro has been used by several UK city authorities, with Sheffield being the earliest user. Within the SWYMBUS project, the outputs from MODEM and PHEM were linked with the Airviro model. However, no attempt was made to incorporate instantaneous emissions within the SWYMBUS emissions and air quality assessment. ADMS-Roads

The ADMS-Roads model is a comprehensive tool for investigating air pollution problems due to small networks of roads that may be in combination with other sites. ADMS-Roads combines many of the scientific capabilities of the larger and more comprehensive ADMS-Urban, and can be used to examine the emissions from many sources simultaneously, including over 7000 road links (150 road sources each with up to 50 vertices), and industrial sources (limited to 3 point, 3 line, 4 area and 25 volume sources). The ADMS models have been extensively used in the UK local air quality management process, with over 70 local authorities having used the model to assist in their first round of Review and Assessment and the identification of the boundaries for air quality management areas and for the development of air quality action plans.

ADMS-Roads has several basic technical advantages over other air dispersion models (such as CALINE, ISC and R91) in that it incorporates the latest understanding of the boundary layer structure, and goes beyond the simplistic Pasquill-Gifford stability categories method with explicit calculation of important parameters. The model uses advanced algorithms for the height-dependence of wind speed, turbulence and stability to produce improved predictions. ADMS-Roads has been developed with a number of features to simplify the modelling process and help users. For example:

• Links with GIS.

• Source and emissions data can be imported from an Access database using a default inventory tool, EMIT, or a user created emission factor database.

• The model incorporates the latest DMRB emission factors (released February 2003).

• ADMS-Roads includes an intelligent gridding option which can place extra output points in and adjacent to road sources.


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• The user defines the pollutant, the averaging time (which may be an annual average or a shorter period), any percentiles and exceedance values that are of interest, and whether or not a rolling average is required.

Currently, ADMS-Urban can model diurnal variations (with different factors for weekdays, Saturday and Sunday) and monthly variations for each road (with up to 50 profiles to assign to the 1500 roads that can be modelled). These variations can be applied to an average emission rate, either calculated by the model itself, or through the use of the EMIT tool. In theory it would be possible to have different emissions for each link of a road (currently road sources are made up of 50 links in ADMS). However, to incorporate instantaneous emissions would undoubtedly require a much larger number of ‘links’, and for practical purposes a restriction on the number of road sources modelled. One problem with the integration of an instantaneous emission model such as MODEM or PHEM with ADMS would be the time scales involved. ADMS-Urban models the dispersion of emissions over time scales of the order of one hour. The algorithms are not currently valid for very short time scales. When considering instantaneous emissions factors that vary on a second-by-second basis, the modelling would have to assume that the meteorology remains constant for the duration of the full drive cycle, and then the results could be scaled. However, in reality this is not wholly correct, as for short time scales, meteorology is subject to significant fluctuations, and so the dispersion of the emissions would change accordingly. Therefore the difficulty in using instantaneous emission factors associated with individual vehicle driving cycles is restricted not by the simulation of the dispersion process, but by the availability of suitable micosimulated meteorological data.

3.2.2 Projects

There have been a large number of national and European projects that have integrated road transport activity, exhaust emissions and dispersion modeling suites. These include various TRAMAQ projects, the EPSRC LANTERN (RETEMM) project and the EU projects EMMA, EFFECT, HEAVEN and OSCAR. However, whilst these projects have attempted to incorporate vehicle dynamics into the emission and air pollution models, none have currently integrated instantaneous emission factors into air pollution dispersion models. Two projects that provide a good basis for current developments are described below.

EFFECT The EFFECT project (Environmental Forecasting For the Effective Control of Traffic) which was funded by the EU as part of the DGXIII Telematics Applications Programme, investigated the links between traffic flow and air quality in four European test sites. The overall aim of the EFFECT project was to predict poor local air quality and then to instigate effective demand management measures to reduce pollution levels in particular problem areas. Using Leicester as a test case, the research developed a real-time link between the SCOOT traffic data and the Airviro model. This enabled the production of information alerts based on the current levels of pollution based on the traffic data measured by SCOOT in the most recent hour and the TRIPS modelled data elsewhere across Leicestershire. Under the remit of the subsequent EFFECT+ project, the DRACULA micro-simulation model was further integrated within the EFFECT system for Leicester. HEAVEN HEAVEN (Healthier Environment through Abatement of Vehicle Emission and Noise)29 was a research project co-funded by the Information Society Technologies Programme of the European Union. It had the high-level goal of demonstrating a Decision Support System (DSS), which could be used to evaluate the environmental effects (air quality and noise quality - both emissions and dispersion forecasting) of Transport Demand Management Strategies (TDMS) in large urban areas. The EU cities of Berlin, Leicester, Paris, Rome, and Rotterdam as well as the CEEC city of Prague serve as the demonstration sites of the project.

29 http://heaven.rec.org/


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Within the Rotterdam case study the TNO VERSIT model was used to derive near real time emission factors, and their incorporation within the dispersion calculation model: the new TNO-Dutch National Model (DNM), adjusted to include line sources. However, the outputs from VERSIT were restricted to hourly average hourly speeds, although adjusted for local vehicle dynamics. Therefore the HEAVEN project did not incorporate available instantaneous emission factors.


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4 Summary and discussion

4.1 Linking micro-simulation traffic and emission models

One of the drawbacks of instantaneous emission models is the requirement for detailed input data on vehicle operation to be specified by the user. Few users have the information required, and one potential solution to this is the use of traffic models, in particular micro-simulation models, to generate the required inputs. However, the application of a micro-simulation traffic model to a particular part of the road network is a time-consuming process which requires some form of calibration.

An essential property of all micro-simulation traffic models is the prediction of the operation of individual vehicles in real time, over a series of short time intervals, and using models of driver behaviour such as car-following, gap acceptance, lane-changing and signal behaviour theories, rather than aggregate relationships. The four traffic micro-simulation models described in this Report – VISSIM, PARAMICS, DRACULA and SISTM – are the best-known in the UK. It is clear that all four traffic micro-simulation models are very detailed, but they do appear to have similar characteristics.

The calculation of emissions is relatively straightforward, once a suitable model has been identified and a correspondence has been established between the traffic model outputs and the emission model requirements. Of the four models, VISSIM appears to have the most sophisticated and up-to-date in-built emission module. However, even this is now several years old. However, the four traffic models have been linked with external emission models in a number of different studies. VISSIM has been linked to MODEM, and more recently three of these traffic models (excluding SISTM) have been linked to CMEM. Although the links with CMEM appear to represent the state of the art, there must be some doubts about the applicability of CMEM in the UK.

The output parameters from micro-simulation traffic models were compared with the input parameters required for emission modelling. The most important factors in relation to the estimation of vehicle emissions (in general) are the road characteristics, the traffic flow, the vehicle operation (e.g. vehicle speed, gear, engine load) and the system of vehicle classification. Road characteristic parameters are not always defined in micro-simulation traffic models, but this is not a major drawback as these are not generally required for micro-simulation emission modelling, and in any case they can generally be otherwise obtained by the model user. The road segment length is an important parameter in any model which is used for estimating emissions, and this is included in all traffic models. One of the most important factors is the road gradient, which has a large effect on emissions from heavy-duty vehicles, and should be taken into consideration. For emission modelling purposes, the total number of vehicles per specified period is required. As this type of information is fundamental to micro-simulation, this aspect of model integration is unlikely to present any difficulties. Traffic density is also used in the traffic situation modelling approach to define vehicle operation, but this parameter is not needed in instantaneous emission models as the driving pattern must be specified anyway. Vehicle operation is one of the crucial considerations when integrating traffic and emission models. For the sake of emissions accuracy it is crucial to take into account the kinematics (the driving pattern) and the power demand. Another important parameter, especially for HDVs, is the vehicle load. In the case of micro-scale modelling, there is a reasonably close correspondence between the outputs of the traffic models (speed as a function of time and location) and the input requirements of emission models. However, information on vehicle loads will need to be obtained from sources other than the traffic models.

One of the main challenges in micro-scale model integration is the matching of the different vehicle types defined in the traffic simulation and emissions components. The systems of vehicle classification required for emission models are generally more complex than those used in traffic models. In emission models, hundreds of different vehicle categories are required to take account of the various factors affecting emissions. Traffic models typically have different vehicle types that are based on how they operate within a roadway network. In addition to the obvious divisions of vehicle types (i.e. motorcycles, passenger cars, buses, HGVs), categories are often made based on vehicle performance (e.g. high-performance cars, low-performance cars) that can be closely related to traffic simulation parameters. In the case of HGVs, transportation models typically categorise vehicles based on their configuration and number of axles. In all cases, a straightforward approach to handling the vehicle matching is to create an appropriate mapping between the vehicle types defined in the traffic simulation model, and the vehicle types defined in the emission model (Barth et al, 2001b). From an


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emissions perspective, vehicles can be therefore separated into ‘coarse’ categories (e.g. cars, LCVs, buses, HGVs, motorcycles), for which it might be possible to obtain correspondence with traffic models, and ‘detailed’ categories (e.g. categories of emission legislation, weight, engine capacity) which relate to emission legislation and would not be expected outputs from traffic models. Neither MODEM nor PHEM cover direct-injection petrol cars, cars using fuels other than petrol or diesel, light commercial vehicles (M, N1), or motorcycles.

In CMEM the categories are based on vehicle types and emission standards which are relevant to the US. For example, CMEM has 26 different categories of light-duty vehicle. The basic car categories are ‘no catalyst’, ‘2-way (oxidation) catalyst’, ‘three-way catalyst’, and ‘Tier 1’. There are also specific categories which relate to high-emitting vehicles. In order to use CMEM in Europe, a correspondence between the US emission standards and EU emission standards must therefore be established a process which is not straightforward for a number of reasons. For example, the US and EU emission standards have different values, were introduced in different years and based on different test cycles. Even where a correspondence can be obtained between the US and EU emission standards, there is no guarantee that real-world (off-cycle) emissions will be equivalent for the corresponding US and EU vehicles, given the different vehicle design standards, possible differences in the operation of fuelling and emission control systems, and differences in vehicle operation. Furthermore, it should be noted that in the US standards a system of ranges and the emission limit value refers to an average value for each manufacturer. This implies that a car manufacturer can produce models with quite different emission characteristics. For these reasons, the use of CMEM in Europe cannot be recommended, despite the fact that some attempts have been made to do so.

The HA has developed its own microsimulation traffic model, SISTM. This tool has been used widely across the HA road network, but has remained as a research tool and has not been subject to wide peer review. However, its performance is at least as powerful as the commercially available tools VISSIM and PARAMICS, although less emphasise has been placed on its visualisation capabilities. An enhanced SISTM three-dimensional visualisation tool has been developed in conjunction with Qinetiq, but further developments and improvements are required. For light duty vehicles, SISTM currently incorporates a version of the MODEM emission model. However, as MODEM was restricted to passenger car data, average speed emission functions are employed, based upon average speed functions from the COST Action 319 and the EU MEET project (Barlow 1997; Jost et. al., 1992; Joumard et. al., 1995). The last major revision to SISTM occurred in 2000, and it is thus now subject to review. It is likely that this review will include the use of a replacement to MODEM, possible through the use of PHEM.

Ideally, the development of an improved scenario evaluation tool would aim to link the most detailed and up-to-date instantaneous emission model to the most appropriate traffic model. The best candidate for the emission model is probably PHEM. However, PHEM is itself rather time consuming to run. It is likely that were PHEM to be fully integrated into a micro-simulation traffic model, and then the speed of the model would be reduced considerably. It is likely that the better option would be to run PHEM ‘off-line’.

4.2 Links between emission and air pollution models

Air pollution dispersion models are widely used for predicting past, present and future concentrations of pollutants, on a range of geographic scales and temporal resolutions. Such models can provide useful insights into the physical and chemical processes that govern the creation, dispersion and transformation of pollutants. For example, the contribution of different source sectors to ambient pollutant levels can be deduced from model calculations.

These models can be simple empirically-based tools, or more sophisticated and complex tools based upon first principles incorporating extensive input data. For road transport applications, the input requirements can again be relatively simple requiring data on traffic flows and speeds, and the use of default parameters to characterise the dispersion process. Alternatively, some dispersion modelling tools, that incorporate CFD and chemical transformation schemes, can require complex and comprehensive input data. Since the early 1940s, analytical solutions to most fluid dynamics problems have became readily available for simplified or idealized situations. The more recent developments in asymptotic/semi-analytical methods, which incorporate perturbation techniques and scale similarity analysis have become readily applied to viscous flow and inviscid compressible flow problems. These applications have been largely facilitated by significant and on-going


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improvements in computer performance. The use of CFD modelling, which had been restricted by its computer processing requirements is now widely used in air pollution assessments. However, whilst these CFD-based tools routinely incorporate complex air flow regimes, the integration of instantaneous emissions from road transport sources has not been developed. Indeed those projects that have employed micro-simulation traffic and emission modelling, have routinely employed time-averaged emission factors for subsequent use in dispersion modelling processes. The link between instantaneous emissions and air pollution dispersion modelling tools remains largely untested.


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Akcelik and Associates (2006). Traffic models. http://www.akcelik.com.au/TrafficModels.htm

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Barcelo (1996). The parallelization of AIMSUN2 Microscopic Traffic Simulator for ITS applications, presented at the 3rd World Conference on Intelligent Transport Systems", Orlando 14-18 October 1996.

Barlow T J (1997). The development of high speed emission factors. TRL unpublished Report PR/SE/333/97. Transport Research Laboratory, Crowthorne.

Barlow T J (1999). The M25 variable speed limit scheme – the effect on vehicle exhaust emissions. Highways Agency project summary note. TRL, Crowthorne.

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Appendix A: Abbreviations and glossary of terms

Table A1: Abbreviations used in the Report.

ARTEMIS Assessment and Reliability of Transport Emission Modelling and Inventory Systems30

NAEI National Atmospheric Emissions Inventory (UK)

CMEM Comprehensive Modal Emissions Model

NOx oxides of nitrogen

CO carbon monoxide NO2 nitrogen dioxide

CO2 carbon dioxide OBD on-board diagnostics

CVS constant-volume sampler PHEM Passenger car and Heavy-duty Emission Model. One of the models derived from COST Action 346 and the ARTEMIS project

DMRB Design Manual for Roads and Bridges

PM particulate matter

DGV Digitised Graz Method THC/HC total hydrocarbons

HBEFA Handbook of emission factors VeTESS Vehicle Transient Emissions Simulation Software

HDV heavy-duty vehicle VOCs volatile organic compounds

LDV light-duty vehicle

MODEM Modelling of emissions and fuel consumption in urban areas

30 European Commission 5th Framework project which will develop a harmonised emission model for road, rail, air and ship transport to provide consistent emission estimates at the national, international and regional levels. http://www.trl.co.uk/artemis/introduction.htm


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Table A2: Glossary of terms used in the Report.

Driving cycle In this Report the term ‘driving cycle’ is used to describe how a vehicle is to be operated during a laboratory emission test. A driving cycle is designed to reflect some aspect of real-world driving, and usually describes vehicle speed as a function of time. The driving cycle may be based upon real-world measurements, or may take the form of an ‘idealised’ schedule of vehicle operation.

Driving pattern Here, the term ‘driving pattern’ is used to describe how a vehicle is to be operated under real-world conditions, based on direct measurement, or the time history of vehicle operation specified by a model user. In the literature, this is also often referred to as a driving cycle. However, in this Report it has been assumed that a driving pattern only becomes a driving cycle once it has been used directly in the measurement of emissions.

Dynamics Variables which emission modellers use to describe the extent of transient operation in a driving cycle (e.g. maximum and minimum speed, average positive acceleration). Can be viewed as being similar to the concept of the ‘aggressiveness’ of driving.

Road characteristics Information relating to the road, such as the geographical location (e.g. urban, rural), the functional type (e.g. distributor, local access), the speed limit, the number of lanes and the presence or otherwise of traffic management measures.

Traffic characteristics/ conditions

Information relating to the bulk properties of the traffic stream – principally its speed, composition and volume/flow or density.

Transient Relates to when the operation of a vehicle is continuously varying, as opposed to being in a steady state.

Vehicle operation The way in which a vehicle is operated (e.g. vehicle speed, throttle position, engine speed, gear selection).


42TRL 42 PPR 269

Abstract

Instantaneous emission models aim to provide a precise description of vehicle emission behaviour by relating emission rates to vehicle operation during a series of short time steps (often one second). In recent years, increases in computing power have enabled more practical use to be made of micro-simulation traffic models. These can be used to assess the effects of various measures applied to the network, such as ramp metering, route diversion, variable speed limits, travel information systems and route guidance, thus leading to improvements in traffic management policies. Such models are the only tools which can be used to assess the impacts of policies and infrastructure changes on individual types of driver, time-varying policies, and complex junctions and layouts.

This Report presents an examination of the links between instantaneous emission models, micro-simulation traffic models and air pollution dispersion models. This Report gives most emphasis on the first two model categories, as the integration with air pollution models has received limited emphasis by the modelling community. This integration with air pollution models remains largely restricted by the physical demands on computing power associated with state-of-the-art computational fluid dynamics-based models. The advantages offered by instantaneous traffic and emission modelling have not been widely incorporated into air pollution modelling tools.

Documents

The links between micro-scale traffic, emission and … 269.pdf2 Links with micro-scale traffic models 5 2.1 General traffic modelling approaches 5 2.1.1 Junction-based traffic models