IS MICRO-SIMULATION A WASTE OF TIME? Ken Fox Halcrow Group

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1 IS MICRO-SIMULATION A WASTE OF TIME? Ken Fox Halcrow Group 1 INTRODUCTION Micro-simulation models are computer models where the movements of individual vehicles travelling around road networks are determined by using simple car following, lane changing and gap acceptance rules. They are becoming increasingly popular for carrying out transport assessments and for the evaluation and development of traffic management and control systems. Figure 1: A micro-simulation model A guiding principle in choosing a model for a transport assessment is the appropriateness of the modelling tool for the job taking into account the scope of the task, the needs of the stakeholders, and the resources available. When considering the use of a micro-simulation model, a key question to ask is: Would a more traditional (non micro-simulation) model meet the requirements? Despite the claims of some micro-simulation developers, for the vast majority of transport assessments there is strong evidence that a pure microsimulation approach is totally inappropriate. For most cases greater Association for European Transport and contributors

2 understanding of the operation and performance of the network can be obtained better and much faster from a more traditional approach. This paper explores this issue in more detail and provides advice on when micro-simulation is most effective. It also describes how new products are coming to the market that provide tools that seamlessly integrate macro, micro and meso modelling together allowing the best approaches to be combined effectively. 2 CAPACITY A major flaw with micro-simulation models is their inability to consider capacity explicitly which is a key indicator when you want to understand how your network is operating. With a traditional traffic assignment model you can see where the network is over capacity and where there is spare capacity that you can utilise. It is very difficult to answer such questions with a micro-simulation approach. Micro-simulation models have no concept of capacity, it is not an input and it isn t calculated, so it is very difficult to understand what is happening in the network from running a micro-simulation model. You can t work out where the bottlenecks are or where they would move to if any were removed. All they will tell you is that there is a queue if you send traffic down a street and it can t all get out the other end. They don t tell you how much spare capacity there is if there is no queue and they don t tell you if there will be other problems in your network if you fix a capacity problem on one link and allow the traffic to get through the first bottleneck. To get a proper understanding of what is going on in the network you need a model that does look at capacity explicitly, i.e. a traditional traffic assignment model. 3 ACCURACY The increased detail offered by a micro-simulation is spurious modelling in finer detail does not necessarily produce more accurate answers - and is not worth the hugely increased computer run-times. Quite often when observing the animated outputs of micro-simulation models you see cars going through one another or running into the back of queues or lane changing at inappropriate places or even doing 360 degree turns as they travel along. This makes it very difficult to get models accepted as being valid as it just isn t realistic behaviour. The look-ahead capabilities also often don t work very well which causes problems when modelling small junctions, particularly mini-roundabouts. Association for European Transport and contributors

3 Figure 2: Problems with car following! Figure 3: Problems with lane changing! Figure 4: Problems with gap acceptance! Association for European Transport and contributors

4 Claims that micro-simulation models pollution emissions more accurately than traditional approaches based on average link speed are doubtful given these other fundamental problems. As flows on motorways increase, it becomes increasingly likely that phenomena such as shockwaves, flow breakdown, phase transitions and synchronised flows will occur. Most micro-simulation models are capable of reproducing fundamental speed/flow and flow/density curves, but their ability to reproduce these more complex phenomena is still unclear. Hoogendoorn & Bovy (2001) claim that traditional car-following rules used in virtually all microsimulation models do not have the required properties to let them reproduce congested traffic flow phenomena. Another problem with many micro-simulation models is that they don t model vehicle behaviour on motorway on ramps realistically. Liu and Hyman (2008) have shown that the gap acceptance approach used by many microsimulation models tends to under-estimate the capacity of the merges and thus over estimate the delays to the merging traffic and under estimate the delays and interruptions to motorway traffic. Suggestions have been made to improve the algorithms used by micro-simulation models for modelling motorway merges. 4 TRAFFIC ASSIGNMENT As computing power has improved, the possibility has arisen of using microsimulation to model very large road networks. It is now possible to model peak periods on quite large road networks with hundreds of junctions and typically tens of thousands of vehicles per hour entering the network at the micro level with a typical office PC. This brings with it all the problems associated with modelling area-wide networks. A key problem is how to assign traffic to routes. Traditional traffic models have tended to rely on the concept of userequilibrium (Wardrop, 1952) to assign traffic to the possible routes between origins and destinations in a road network. This predicts a long-term average state of the network and assumes steady state network supply and demand conditions, from day-to-day and within different periods of a day. Equilibrium assignment models usually use an iterative procedure to assign traffic from an O D matrix to routes across the network. For example, the SATURN assignment model uses the following process. It is assumed that the costs of travelling along any link within the network are based on a simple function of the travel time and distance travelled along that link. For the first iteration it is assumed that the travel time along each link is just the free flow travel time. The minimum cost path is therefore calculated for each O D pair and all traffic for that O D pair is assigned to this minimum cost path. Traffic travelling down a link will approximately obey a simple speed-flow relationship that is characterised by a power law equation. Now that traffic has been assigned to each link in the network, such a speed flow relationship can be Association for European Transport and contributors

5 used to determine the new link speeds and hence the new costs of travelling down each link. So a new set of minimum cost paths can now be determined. Traffic can now be proportioned between the original paths and the new paths in such a way as to minimise an objective function. This results in a new set of link flows, which will result in a new set of costs and a new set of minimum cost paths. This process continues until some convergence criterion is satisfied. To improve the accuracy of the process, a simulation model is used to provide the assignment model with accurate flow-delay curves for each link (and corresponding turn at the end of the link) in the network. This results in a further iterative loop. At the end of each set of assignment iterations, the assigned flows are passed to the simulation model. This then simulates the traffic flowing through the network and determines the coefficients that can be used to characterise each flow-delay curve used for each link in the network. These flow-delay curves are then passed back to the assignment model so that a new assignment of traffic can be calculated. This process continues until the assignment produces flows that do not change much from those calculated in the previous iteration. The assignment process is then said to have converged. The Design Manual for Roads & Bridges (Volume 12, Section 2, Part 1 Traffic Appraisal in Urban Areas, Chapter 4 Traffic Model Development, ) gives advice on suitable criteria to use to test for equilibrium assignment convergence. Many of the current micro-simulation models have a built-in route choice model. These however are not traditional equilibrium assignment models but rather ad-hoc dynamic assignment models. They have usually been designed to provide immediate vehicle route choices for individual vehicles as they travel through a dynamically changing network. For example a car might decide, perhaps in response to information received from a route guidance system, to divert away from its chosen route to its destination in response to an incident that would cause delays if the vehicle stayed on its original course. These dynamic route choice methods used by micro-simulation models are somewhat experimental when compared to tried and tested equilibrium approaches. Typically as vehicles are generated they are given a path to their destination based on the costs of using alternative paths. The values of these costs are obtained from the travel times of vehicles that have previously travelled along the arcs that make up the path. The costs of travelling along arcs can either be derived from free-flow travel times, from costs experienced during a simulation warm-up period, costs experienced during a previous time interval during the current simulation run or costs experienced at the same time in a previous run. At user defined time intervals the minimum cost path to every destination in the network is determined by the program. These minimum cost paths are added to the list of available paths. The current costs of travelling along all the available paths are also calculated and when vehicles are generated in the subsequent time interval they are distributed amongst the previously discovered minimum cost paths to their chosen destination, according to the current cost of travelling along each path. A number of alternative functions (e.g. logit, binomial, c-logit, Kirchoff s law) are available that can be used to distribute the vehicles amongst the available paths according to their relative costs. This process is very time consuming. Association for European Transport and contributors

6 Calculating the costs second by second for every vehicle in the network takes much more time than calculating costs based on flow-delay relationships and it is often no more accurate. They clearly differ from the traditional equilibrium approach to route choice that tries to determine the routes that traffic will settle down to using in the months following a change to the road network, such as the addition of a new road. There is no use of speed-flow relationships to determine link costs and redistribute traffic and no iterative procedure is proscribed to try to get flows to converge as the iterations progress. In theory, these dynamic route choice models can be used to provide assignment in a static network model, instead of using a traditional equilibrium approach. In practice, this approach has been found to be extremely problematical. In the peak periods networks are usually operating close to capacity. In this case it can be all too easy for the dynamic assignment model to produce gridlock situations from which the network model cannot sensibly recover. They also have problems with convergence. As with traditional assignment models, it is possible to alleviate such problems by using special techniques such as incremental loading. Incremental loading works by carrying out a series of simulation runs. On the first simulation run only a fraction of the traffic is loaded onto the network. At the end of the simulation run the costs of travelling along each link are determined and used to influence route choice in the next simulation run, where a greater proportion of the traffic is now loaded. As each run has a good estimate of link costs gathered from previous runs, the model is less likely to overload links close to capacity. The process continues until a series of runs can be carried out with the full traffic load. However, in practice this approach also has problems when used with micro-simulation. Often significant differences in results are obtained for small differences in the chosen loading method. Also this method often fails to converge once full loading is complete and gridlock frequently reoccurs. The dynamic route choice algorithms utilised by the commercially available micro-simulation models are not based on any sound principles such as Wardrop Equilibrium. A major weakness with micro-simulation models is therefore their lack of any academically accepted traffic assignment methods. They also can t deal with fundamental issues such as variable demand modelling or modal split. Variable demand modelling is becoming increasingly important. How individuals change their travel behaviour in response to changes in their travel environment is an active area of research. Changes in travel times due to congestion may cause individuals to change their route, departure time, destination, travel mode, or any combination of these. All these problems lead to the conclusion that micro-simulation is inappropriate for any networks where route choice is a key issue. From both a practical and a theoretical viewpoint micro-simulation is certainly not currently Association for European Transport and contributors

7 suitable for using on large networks operating close to capacity such as whole town centres. 5 AN INTEGRATED APPROACH Given the problems encountered with micro-simulation based dynamic assignment methods in congested networks, alternative approaches need to be used to assign traffic when using micro-simulation. Some of the developers of micro-simulation models have realised this and are now beginning to produce products that allow an integrated approach, where assignment can be carried out with a macro or meso simulation and the results used by their micro-simulators. Ideally a single network representation should be used and different approaches used to do different tasks such as assignment and simulation. This is the approach used by Aimsun ( which has Macro, Meso and Micro simulators within a single integrated environment. Both static and dynamic assignment can be carried out using a variety of approaches and the resulting paths and matrices used by any of the simulators. Consistency between the different simulators in Aimsun is automatic due to their common network representation. This overcomes the tricky business of keeping the various models consistent, which can be a problem with alternative approaches such as that used by the VISSIM micro-simulator ( which can take assignment results from the macro VISUM simulator. Micro-simulators can therefore make use of the assignment outputs of existing traditional models. This however can be quite time consuming as the network has to be built using both the traditional model and the micro-simulation model. Some micro-simulation models have made this process easier by automatically linking to a traditional model. This is the case with Aimsun and EMME/3 ( and SATURN ( Aimsun networks can thus be automatically translated into the equivalent EMME/3 or SATURN networks and vice-versa. So a network model can either be built with Aimsun and the equivalent EMME/3 or SATURN network model automatically generated or Aimsun can import an existing EMME/3 or SATURN model directly. EMME/3 or SATURN can be run and the results of its equilibrium assignment can be automatically fed back into Aimsun. As well as problems maintaining consistency between the different models, the cost of purchasing two models to carry out a study can make this approach expensive. 6 RECOMMENDATIONS Micro-simulation models model each individual vehicle within a network. In theory, such models provide a more realistic representation of actual driver behaviour and more accurate network performance predictions. In practice they are much slower than traditional models and it is much more difficult to obtain robust results from them, particularly in networks that are congested and where route choice is a factor. Association for European Transport and contributors

8 It is therefore recommended that alternative approaches are used to model congested networks where route choice is a factor. Integrated models, such as Aimsun that can perform assignment using traditional macro Wardrop equilibrium approaches or meso-scopic simulation with improved stability and more reliable convergence appear to be very promising. The assignment can then be passed on to the microscopic model and used to perform more detailed analysis. This integrated approach is much faster, more reliable, more stable and produces much more statistically robust results. Considerable time savings and more robust conclusions are obtained by using models that effectively combine different approaches to traffic modelling within the same network model. Micro-simulation models are ideal for investigating the performance of small to medium sized traffic networks containing advanced traffic management and control systems. However, experience has shown that trying to model large congested networks with a pure micro-simulation approach is likely to be a huge waste of time. Bibliography Hoogendoorn, S.P. & Bovy, P.H.L. (2001) State-of-the-art of vehicular traffic flow modelling, Journal of Systems and Control Engineering, 215, Liu, R. and Hyman G. (2008) Generic guidance for modelling merges, Proceedings of the European Transport Conference 2008, Leeuwenhorst Conference Centre, The Netherlands, 6-8 October Wardrop, J.G. (1952) Some theoretical aspects of road traffic research. Proceedings Institute of Civil Engineers, Part II(1), Association for European Transport and contributors