APPLICATION OF A SIMULATION-BASED
DYNAMIC TRAFFIC ASSIGNMENT MODEL
Michael Florian, Michael Mahut and Nicolas Tremblay INRO Consultants, Inc. 5160 Deacute;carie Blvd., Suite 610, Montreal, Quebec, H3X 2H9 Canada
(mike, michaelm, nicolas)@inro.ca
ABSTRACT
The evaluation of on-line intelligent transportation system (ITS) measures, such as adaptive route-guidance and traffic management systems, depends heavily on the use of faster than real time traffic simulation models. Off-line applications, such as the testing of ITS strategies and planning studies, are also best served by fast-running traffic models due to the repetitive or iterative nature of such investigations. This paper describes a simulation-based, iterative dynamic-equilibrium traffic assignment model. The determination of time-dependent path flows is modeled as a master problem that is solved using the method of successive averages (MSA). The determination of path travel times for a given set of path flows is the network-loading sub-problem, which is solved using the space-time queuing approach of Mahut. This loading method has been shown to provide reasonably accurate results with very little computational effort. The model was applied to the Stockholm road network, which consists of 2100 links, 1,191 nodes, 228 zones, representing and 4,964 turns. The results show that this model is applicable to medium-size networks with a very reasonable computation time.
Keywords – dynamic traffic assignment, method of successive averages, traffic
simulation, queuing models
2 SIMULATION APPROACHES IN TRANSPORTATION ANALYSIS
INTRODUCTION
The functional requirements of a dynamic traffic assignment (DTA) model for ITS applications may be subdivided into two major modes of use: off-line and on-line. The off-line use of DTA is for the testing and evaluation of a wide variety if ITS measures before they are implemented in practice. In particular, iterative approaches to dynamic assignment that approximate (dynamic) user equilibrium conditions are generally restricted to off-line use due to the high computation times involved. The resulting assignments can also be interpreted as imitating driversrsquo; adaptation over time to changes in network topology or control, including the implementation of ITS measures. Due to the high number of iterations usually required, such applications are ideally suited for traffic models that have low computational requirements. On-line DTA can be used within a system that monitors and manages the network in real time. DTA and the embedded traffic models can play a key role in providing short-term forecasts of the system state that are used by adaptive traffic management, control and guidance systems. Due to the need to provide feedback in real time, on-line DTA poses rather stringent demands on the embedded models for maintaining low computation times.
The need to model the time varying network flow of vehicles for ITS applications has generated many contributions for the solution of dynamic traffic assignment methods. These contributions are varied and have been motivated by different methodological approaches. They may be classified according to the modeling paradigm underlying the temporal traffic model. In order to provide a common terminology to the various models, it is convenient to refer to two main components of any dynamic traffic model: the route-choice mechanism and the network-loading mechanism. The latter is the method used to represent the evolution of the traffic flow over the links of the network once the route choice has been determined.
Perhaps the most popular dynamic traffic models today are those based on the representation of the behavior of each driver regarding car following, gap acceptance and lane choice. These are micro-simulation models such as CORSIM (http://www.fhwa-tsis.com/corsim_page.htm), INTEGRATION
Application of a Simulation-Based Dynamic Traffic Assignment Model 3
(Van Aerde, 1999), AIMSUN2 (Barceloacute; et al, 1994) (http://www.tss-bcn.com), VISSIM (http://www.ptv.de), PARAMICS (http://www.quadstone.com) and DRACULA (http://www.its.leeds.ac.uk/software/dracula/). MITSIM (Yang, 1997) (http://web.mit.edu/its/products.html) is an academic research model that has been used in several studies in Boston, Stockholm and elsewhere.
There are many other micro-simulation models developed in universities and industrial research centers that use the same basic approach. The route choice in a micro-simulation model is either predetermined or computed while the loading of the network is being carried out. Essentially, a micro-simulation model aims to provide the traffic flows composed of individual vehicles in one network-loading step. As micro-simulation models are built by using many stochastic choice mechanisms, their proper use requires the replication of runs. The successful use of micro-simulations is commonly limited to relatively small size networks. Their application has been hindered for medium-to-large networks by the relatively high computation time and effort required for a proper model calibration. Usually, there are many parameters involved. Choosing appropriate parameter values is a relatively complex task since each computer implementation for a micro-simulation uses a large number of heuristic rules that are added to the basic car-following mechanism. A thorough understanding of how these parameter choices influence the results, when using given software package, is essential for a successful application. Nevertheless, micro-simulation models are popular and their use is enhanced by traffic animation graphics that capture the attention of non-technical staff.
The aim of handling larger networks with r
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