Scales of Traffic Flow SimulationEd Manley

Department of Civil, Environmental and Geomatic EngineeringUniversity College London

September 2009

With the development of pervasive telecommunications andparallel computing, the last twenty years have seen bigstrides in the extent to which our roads may be monitored andmanaged. Urban Intelligent Transportation Management Systems(ITMS) enable transport managers to more effectively managetraffic flows and quickly detect and respond to incidents onthe network. Despite these developments, urban congestion isincreasing rapidly [Schrank and Lomax 2005] with little room(in most cities) to build further network capacity. Therehave even been accounts of increased journey delay occurringdespite falls in the number of vehicles entering the urbanarea [Transport for London 2008]. These problems suggest theneed for new approaches to address this issue.

Traffic analysis tools have traditionally provided engineerswith a toolkit for assessing and planning road trafficnetworks. This broad field encompasses a number oftechnologies, analysing traffic at wide ranges of scale anddetail. One side of this analysis may be carried out byplanning and management tools [Jeannotte et al. 2004] aimed athelping plan large-scale transport and urban developments.These tools include network ‘sketch’ simulations, whichprovide planners with static capacity values, and traveldemand models, which attempt to predict demand on a roadnetwork in terms of travel time, mode of travel and routechoice. Along side these tools are those that look more indetail at traffic simulation, focussing more on estimation oftraffic flows and densities across various parts of the roadnetwork. Traffic flow models may be classified by threescales – macroscopic (aggregated data, low specificity);mesoscopic (some aggregation, medium specificity); andmicroscopic (no aggregation, highly specific simulation ofindividuals). It is on these three scales of flow simulationthat this report will focus.

Scales of Traffic Flow Simulation – Ed Manley 1

In the first instance, this report will examine, in depth, thekey features, applications, advantages and drawbacks ofmicroscopic, macroscopic and mesoscopic traffic flowsimulation. With these points in mind, there will be furtherexamination of the considerations a modeller should take indeciding which type of simulation model to use, with a view toclarifying the circumstances by which each model is bestsuited.

Microscopic Simulation models

Microscopic simulations attempt to model traffic at a highlevel of detail. Rather than looking at the general flows oftraffic, as is the case with macroscopic simulation, thesemodels attempt to simulate the behaviour of actors within thegiven road network. The aggregation of these simulatedactions help to demonstrate how drivers use the road networkand react to each other in any given situation.

While there are many constituents of a microscopic simulation,the key area of development has been in the realm of vehiclebehaviour. These models have built on the foundations laid inthe simple car following models developed in the 1950s, whichdescribed the process of maintaining a safe distance betweencars. Since these mathematical models, there have beendevelopments in the understanding of driver behaviour, aspectsthat have been incorporated into these models. For instance,along side the car following models are other which deal withlane changing behaviour. These latter models may still befounded on the principles of physics, but they effectivelydescribe the circumstances surrounding the changing of a lane.Recent developments have incorporated other developments incomputer science, such as cellular automata [Nagel andSchreckenberg 1992] and multi-agent systems. These latterapproaches are generally considered to provide a morerealistic of the relationship between drivers on a stretch ofroad, by considering the effects of stochastic decision-makingand road conditions. Some of these latter developments, forexample, have even sought to include aspects such as driverpersonality, in the theory that an aggressively minded person,might act more aggressively on the road, for example. Theaccumulation of these forces lead to a more holistic driverbehaviour.

The general approach of microscopic modelling, where eachvehicle is handled individually, is where its advantages lie.

Scales of Traffic Flow Simulation – Ed Manley 2

With its accuracy in modelling individual entities and theirrelationships with network properties, these models are oftenseen as more ‘realistic’ representations of real worldsituations than that of meso- or macroscopic modelling. Incombining the actions of individual drivers it is possible todevelop a picture of overall traffic flow. For this reason,microscopic simulation is favoured in operational research(e.g. congestion analysis) and is an important aspect in theplanning process. Using these models it is possible toinspect how users might act at new road developments, forexample, or identify if and why particular features contributetowards congestion. As described by the FHWA [Jeannotte etal. 2004], microscopic modelling has usually been favoured inanalysing the impact of smaller network objects, such as buslanes and tollbooths.

While the level of detail, or fidelity, offered by microscopicmodelling would undoubtedly be preferable in many situations,they do not always represent the best tool for trafficanalysis. The problem lies in its advantages. In modellingthe individual vehicles and tracking their behaviours throughthe network, microscopic modelling requires large resources interms of time, money, computational power and data. In orderto ensure that a true picture is being represented, accurategeographic, flow, traffic signal and origin-destination datamust all be available. This must then be treated andconverted into suitable formats to be processed by thesimulator – geographic data for example must be entered innetwork and node form. This takes significant time and staffresources to carry out. Once this is complete, high levels ofcomputational power are required in order to run thesimulation at an adequate speed. This is particularly true ifit is desired that the model be run close to real-time speeds.Then, finally once results are derived, a model will requiremultiple stages of calibration to the large number ofvariables it employs before ‘real world’ results aresimulated. This adds further to the requirements upon timeand staff resources. The demands that microscopic simulationplaces upon a traffic analyst therefore can often decrease itsappeal. As Dowling [Dowling et al. 2002] suggests, “theanalyst should employ only the level of effort required by theproblem being studied”, suggesting that sometimes microscopicsimulation is not worth the effort. As will be detailedlater, depending on the problem being investigated, meso- andmacroscopic simulation can equally provide suitable analysisof the problem at hand.

Scales of Traffic Flow Simulation – Ed Manley 3

Concerns for the use of microscopic simulation also exist atthe fundamental level of model accuracy. It has been raisedat various levels [Fox 2008, Sterzin 2004] that, despite theirtheoretical ability to better analyse the behaviour ofdrivers, not enough information exists to effectively validatethese simulations. For example, while it is very reasonableto attempt to model ‘rubbernecking’ on a microscopic level interms of decreased acceleration, gap acceptance and route-choice, the extent to which these have an effect is less easyto predict. The problem is that these phenomena showthemselves on a macroscopic level, with very little dataavailable on individual behavioural scale e.g. how can theamount someone slows through ‘rubbernecking’ be quantified?While this issue is not unsolvable, it does represent adevelopment issue with this level of simulation that is yet tobe fully addressed.

Figure 1: Screenshot from AIMSUN microscopic simulation software [WuPing Xin 2009]. Each vehicle is modelled, with dashboard viewavailable through built-in GUI.

Macroscopic Simulation models

As the name would suggest, macroscopic models simulate trafficat a much higher level than microscopic simulations. Insteadof dealing with the behaviours and movements of individualvehicles, macroscopic modelling is concerned with aggregatedtraffic flows. These simulations use mathematical models thatrepresent the relationship between speed, flow and traffic

Scales of Traffic Flow Simulation – Ed Manley 4

density, to indicate the general progression of traffic acrossa road network. At this level of representation thesesimulations are not able to model the effect of certaintraffic measures and events (i.e. a bus pulling out, a cyclistbeing overtaken etc.), but can still accurately judge thecapacity and thus performance of a network.

Macroscopic modelling uses aggregate data to describe thebehaviour of large numbers of vehicles in terms of flow,density and speed. Within this model it is assumed that eachdriver will behave in the same way in a given situation,therefore behavioural factors (e.g. speed, overtakingbehaviour) are equal across the flow of traffic. In takingaway the random behaviour of individuals, macroscopic modelsare deterministic by nature. Using average flow, speed anddensity data in addition to ‘known’ values such as traffic jamdensity, road space and maximum speed, it is possible to applymathematical principles to describe the relationship of atraffic flow across space and time.

Given its ability to describe the behaviour of collectives,macroscopic simulation has primarily been used to test theeffects of collective hindrances. For example, using initialflow values of motorway inflows and outflows an analyst isable to quickly derive, from macroscopic simulations, what theeffect of closing a motorway lane might be upon the generalflow, speed and density of traffic. So, in using this model,while it wouldn’t be possible to see the effect of this policyupon each car, the analyst would see a change in generaltraffic performance. This makes macroscopic simulation anexcellent tool in the analysis of network-wide performanceissues (such as congestion). Further, the deterministicnature of the model enables effective with- and withoutincident analysis.

The high-level approach of macroscopic simulation does,however, have significant drawbacks. Its aggregate simulationof traffic flows, as described above, naturally ignores thebehaviours and interactions of drivers on the road. Thus, anychanges in behaviour, relating to lane changes (say to avoid awork zone) or change of route (to avoid congestion) forexample, are discounted. As Benjaafar [Benjaafar et al. 1997]points out, this is useful in demonstrating “long run” trafficflows but cannot be used for traffic analysis and control. Asdescribed earlier, the causes of congestion are demonstratedat a microscopic level (changes in lane, route, individual

Scales of Traffic Flow Simulation – Ed Manley 5

speeds etc), the effect of each one of these could not beadequately described using macroscopic simulation.

Figure 2: Screenshot from Synchro macroscopic simulation software[Synchro 2009]. Note the lack of vehicular simulation, instead flowvalues are provided.

Mesoscopic Simulation tools

Mesoscopic simulations fall between macro- and microscopicmodels in terms of the detail of simulation achievable. Thereare numerous methods by which this is achieved, however,generally speaking, vehicles are modelled individually as withmicroscopic modelling, but governed by rules similar to thoseseen in macrosimulations.

In terms of vehicle and driver behaviour, mesoscopicsimulation takes a higher-level view than that seen withmicroscopic modelling. Vehicles are modelled variously asjoining packets (as seen with the CONTRAM model), cells (aswith DYNAMIT) or individually (various models) in making theirway around the road network. Within these groupings, usuallythe length of a link, vehicular behaviour modelling is minimal

Scales of Traffic Flow Simulation – Ed Manley 6

and speeds determined as a function of traffic density (thedenser the traffic, the lower the speed) within a given area.These methods are very similar to the way in which traffic ismodelled in macroscopic simulation.

Mesoscopic simulation retains the detail of microscopicsimulation within the demand section of the model. Here, asopposed to the macroscopic approach of flow and densityvalues, the route and travel choices are simulated on avehicle-by-vehicle basis. This means that, while losing theability to see the interactions between driver and roadfeatures, it is possible to track dynamic changes in routechoice by drivers depending on changes in the network.Further microscopic additions have been introduced at nodeinteractions (in the case of DYNEMO [Adams Boxhill and Lei2000]), where vehicle-to-vehicle interactions are handledaccording to microscopic priority rules.

Mesoscopic simulation does come across the same accuracyproblems as those seen with macroscopic simulation. Wherebehavioural and interaction detail is removed there areinevitably going to be generalisations that might lead toincorrect analyses of certain situations. Situationsdeveloping within links, for example, cannot be easilyanalysed. So, while mesoscopic simulation might seem like aless resource-heavy alternative to microsimulation it isimportant to remember its limitations in analysing specificproblems.

Discussion

As described above, traffic simulation tools can be veryeffective at demonstrating and predicting the flow of trafficthrough a road network. The information they yield can behighly beneficial to traffic planners, and reveal the causeand effect of previously unforeseen problems. What will beclear at this point, however, is that their use is dependenton a number of factors. While one level of simulation maywork well in one setting, its implementation into anotherscenario may be misleading, even erroneous.

Detailed below are some of the key issues that should beconsidered in the use of traffic simulation tools. For eachpoint there are important aspects to consider in terms of thescale of simulation used:

Scales of Traffic Flow Simulation – Ed Manley 7

Available resources The resources available to anorganisation or research group will have a heavy impact on thetype of analysis they are able to carry out. As describedabove, microscopic simulations have the heaviest drain in allregards. These models require very careful consideration ofthe parameters being input, and a methodical approach to datapreparation. The tools further require high levels ofcomputational power to carry out the simulation within areasonable timeframe. These levels of man-hours and computerpower are often not cost beneficial for many organisations.On the other hand, macroscopic simulations require far less inthe way of resources. As a general rule, they would be ableto run on a laptop and still provide performance statisticsfor a large network.

Area modelled The amount of resources expended on asimulation project, however, clearly is a function of the sizeof simulation being carried out. Taking the case ofmicroscopic simulations again, modelling a very large-scalenetwork would be realistic in many cases, being not onlycomputationally intensive but also costly in terms ofpreparation time. Microscopic simulations are generallylimited to networks of hundreds nodes with under 100000vehicles per hour – not small, but equally not city size [Fox2009] – however, at this scale the costs could be huge.Conversely, macroscopic modelling would certainly lack thedetail required for effective analysis of a small-scale area.Macroscopic models are designed to quickly and effectivelydescribe network-wide traffic phenomena. Mesoscopicsimulation, meanwhile, too lacks the adequate microscopicdetail to provide strong small-scale analysis. It willprovide better analysis of vehicle route-choice and nodeinteractions than macroscopic modelling, but this increasedcomputational requirement makes network-wide analysis lessrealistic.

One further consideration, along with the size of the areabeing analysed, is the traffic phenomena is being evaluated.Network-wide issues, for example, cannot be suitablydemonstrated with an analysis at local level. This accountsfor issues such as congestion, which is the effect of limitedcapacity somewhere on the network, not necessarily the areabeing microscopically modelled.

Scales of Traffic Flow Simulation – Ed Manley 8

Other factors There are also a number of other issuesthat might be of concern to an analyst undertaking trafficsimulation work. These may be less important than thoserelated to scale and resources, but require considerationnonetheless. Firstly, it will be important to considerwhether to include stochastic elements within the model.While most macroscopic models are deterministic, microscopicmodels may be either. Clearly this choice can have a bigeffect upon the results. A second consideration might be thescale of the independent variables (e.g. time, position,speed) [Hoogendoorn 2001]. These may be handled continuously(i.e. the system state changes continuously) or discretely,where changes are made at discrete time instances. Again, thechoice here will make an impact of the results that may bederived from the simulation.

It is on these key factors that the choice of simulation modellays. At one end of the scale are microscopic models, whichaim to ‘build from the bottom up’, encapsulating all driverbehaviour and capturing the traffic phenomena of interest(congestion, say) as an accumulation of these interactions.The current range of models are not ideal however, and arecertainly computationally intensive and time-consuming toprepare and calibrate. But the results can be very telling,and there is certainly more interest within the field ofpursuing this goal, with the support of emerging ArtificialIntelligence technologies.

At the other end of the scale are macroscopic models. They donot seek to explore the detail within, rather they see it aspart of the flows they measure. These models are proven andtheir results given in quantifiable terms, which make thempopular to traffic managers. While this might be true, it canalso be suggested that the stochastic, decision-makingfunctions of individuals, an all-important part of transport,are lost in the aggregation.

In between are mesoscopic models that, in trying to improvethe portability of microscopic models, lose some of the detailthat make the latter so attractive. They do have theirbenefits – they tend to use dynamic route choice methods,which are an advantage over macroscopic models, for example.However, surely with the increasing computational poweravailable to microscopic simulations, the necessity for thesemodels will look increasingly precarious.

Scales of Traffic Flow Simulation – Ed Manley 9

By way of helping clarify the suitability of each to a givensituation, a summary table has been provided below (Table 1),that lays out they key differences between each scale ofmodel:

Scale of SimulationMacroscopic Mesoscopic Microscopic

Simulated unit Traffic flows

Traffic flows with vehicular route and node interaction simulated

Individual vehicle behaviour and route simulated

Fundamental laws drawn from

Physics, Mathematics

Physics, Mathematics

Cognitive studies, Physics, Artificial Intelligence

Input volume of data Low Medium High

Computational need Low MediumGenerally, higher than others

Stochastic behaviour

Not widely incorporated

Depends on route-choice model

Usually stochastic

Model application

Network capacity Strong Strong Poor

Multi-agent and object interaction Moderate/Poor Moderate Strong

Impact of incidents

Good at wide network level Good Good at local

network level

Route choice Moderate Strong Strong

Reaction to changes in flow conditions

Poor Moderate Strong

Scale suitability

Small scale analysis Very poor Medium/Poor Very strong

Medium scale analysis Medium Strong

Strong but computationallyintensive

Large/City scale analysis Very strong Medium

Strong, but computationallyintensive

Scales of Traffic Flow Simulation – Ed Manley 10

Commercial exposure [Jeannotte et al. 2004]Number of commercial applications

12 3 35

Key commercial applications

TRANSYT, FREQ12, Synchro

DYNASMART, DYNAMIT, CONTRAM

AIMSUN, VISSIM,CORSIM, Paramics

Table 1: Summary of the differences between micro-, meso- andmacroscopic simulation.

Conclusion

Traffic flow simulations can be highly beneficial to trafficmanagers and town planners, revealing phenomena and patternsthat otherwise might be missed. However, it is also clearthat there is no simple answer when it comes to selecting aparticular traffic simulation tool for a given situation.There are a wide number of factors relating merely to theselection of the scale of simulation tool, let alone theselection of the tool from within each scale.

What is clear is that microscopic simulation represents thebest choice for the simulation of realistic road conditions,as it incorporates driver behaviour in the most detail.However, it has been argued [Hoogendoorn 2001] that theresults yielded from these simulations are not as accurate asthose at macroscopic levels. Further, macroscopic scalesimulation usually represents a much cheaper and simplerchoice when it comes to practical application of simulation.

In the end the choice of traffic simulation probably comesdown to the size of the network being modelled. At this pointin time, the benefits of the best microscopic simulationmodels cannot be applied at large scales. This may changewith time and developments in computational power, there havebeen reports of low-detail microscopic models of almost amillion agents [Kato et al. 2008], but for the moment,macroscopic modelling is likely to provide the best picture oftraffic flows over large networks.

References

Adams Boxhill S. and Lei Y. (2000), An Evaluation of Traffic Simulation Models for Supporting ITS Development, Center for Transportation

Scales of Traffic Flow Simulation – Ed Manley 11

Training and Research, Texas Southern University, Technical Report No.: 167602-1.

Benjaafar S., Dooley K. and Setyawan W. (1997), Cellular Automata for Traffic Flow Modeling, Intelligent Transportation Systems Institute, Report No.: CTS 97-09, 1997.

Dowling R., Holland J. and Huang A. (2002), Guidelines to Applying Traffic Microsimulation Modeling Software, Available at: http://www.paramics-online.com/downloads/technicaldocs/Caltrans%20Microsimapps%202002.pdf, (Last accessed: 14/09/2009).

Fox K. (2008), Is Microsimulation a waste of time?, European Transport Conference 2008, Available at: http://www.etcproceedings.org/paper/download/3573, (Last accessed: 14/09/2009).

Hoogendoorn S. P. (2001), State-of-the-art of Vehicular Traffic Flow Modelling, In: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, Eds: Papageorgious M. and Diakaki C., Volume 215, Number 4, Pages 283-303.

Jeannotte K., Chandra A., Alexiadis V. and Skabardonis A. (2004), Traffic Analysis Toolbox - Volume II: Decision Support Methodology for Selecting Traffic Analysis Tools, Federal Highways Administration, Technical Report No.: FHWA-HRT-04-039.

Kato S., Yamamoto G., Mizuta H. and Tal H. (2008), Simulating whole city traffic with millions of multiple vehicle agents, January 2008, IBM Research Report Number: RT0759.

Nagel K. and Schreckenberg M. (1992), A cellular automaton model for freeway traffic, Journal de Physique I France 2: 2221-2229.

Schrank D. and Lomax T. (2005), The 2005 Urban Mobility Report, TexasTransportation Institute, May 2005.

Sterzin E. D. (2004), Modeling Influencing Factors in a Microscopic Traffic Simulator, Masters of Science in Transportation Thesis, Massachusetts Institute of Technology, Submitted June 2004.

Synchro (2009), Synchro 7 Screenshot, Available at: http://www.trafficware.com/synchro7.html, (Last accessed 14/09/2009).

Scales of Traffic Flow Simulation – Ed Manley 12

Transport for London (2008), Impacts Monitoring Sixth Annual Report, July 2008, Available at: http://www.tfl.gov.uk/assets/downloads/sixth-annual-impacts-monitoring-report-2008-07.pdf, (Last accessed: 14/09/2009).

Wu Ping Xin (2009), AIMSUN Screenshot, Available at: http://www.wupingxin.net/images/aimsunremote.png, (Last accessed 14/09/2009).

Scales of Traffic Flow Simulation – Ed Manley 13