Simulation Approaches in Transportation Analysis

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.

Recent years have seen a tremendous interest world wide in the use of microsimulation techniques to model traffic in congested road networks. This interest is particularly associated with the development of real-time, high-tech based traffic management and control strategies that react to the highly dynamic and variable nature of traffic conditions and driver behaviour. This paper introduces the DRACULA dynamic traffic network model, with the main focus on its traffic microsimulation component. The paper describes the model’s theoretical and behavioural foundations, and presents illustrative examples of applying the model in the evaluation of real-time management strategies. Our experience shows that this is a particularly suitable framework for the realistic modelling of real-time technological strategies.

The deployment of ITS must be assisted by suitable tools to conduct the feasibility studies required for testing the designs and evaluating the expected impacts. Microscopic traffic simulation has proven to be the suitable methodological approach to achieve these goals. This paper discuses some of the most critical aspects of the dynamic simulation of road networks, namely the heuristic dynamic assignment, the implied route choice models, and the validation methodology, a key issue to determine the degree of validity and significance of the simulation results. The paper is structured in two parts, the first provides an overview on how the main features of microscopic simulation have been implemented in AIMSUN, and the second is devoted to discus in detail the heuristic dynamic assignment.

Microscopic traffic simulation is an important tool for traffic analysis, particularly in the presence of intelligent transportation systems (ITS). In this paper we describe the main components of a microscopic traffic simulation model and illustrate the discussion with examples drawn from our experience with a microscopic traffic simulation tool, MITSIMLab. With respect to the use of simulation models in practical applications we discuss issues related to their calibration and validation. We demonstrate this part with a case study of applying MITSIMLab in Stockholm, Sweden.

This paper, firstly, describes the current status of the utilization of traffic simulation models. Evaluation for the model’s application in Japan was based on a questionnaire survey. Secondly, the Best Practice Manual for Simulation Application, which is currently being developed, is discussed. In addition some exerts from the manual in regards to addressing the issues of simulation application are presented: i.e. i) understanding the models’ nature through verification and validation; ii) OD estimation from vehicle counts; iii) model parameter calibration; and iv) indices to measure the reproducibility. Finally this paper introduces the Clearing House of Traffic Simulation Models. These models promote simulation utilization.

Most studies of traffic network simulations aim to calculate travel times or traffic volumes as accurately as possible using origin-destination (OD) traffic volumes (i.e., an OD matrix). In general, estimating OD volumes is very difficult, and the performance of the simulation largely depends on OD estimation. This study proposes a transportation network simulation model that makes use of OD estimation. This simulation model estimates OD volumes using the absorbing Markov process, which can easily estimate OD volumes using only traffic counts at intersections, and which simulates the transportation network dynamically. This enables us to simulate the network states more closely to actual traffic counts.

Mobile communication technology has recently become applicable in tracking surveys of individual travel behaviour in urban space. This is due to an increase in capacity to collect more precise time and location data of moving objects. This paper presents a study of a tracking survey system, which uses the location positioning function of a PHS (Personal Handy phone System) to collect travel data from 96 spectators at a Sumo tournament held in Osaka. It is shown that the space-time distribution map of location positioning data is useful in understanding the concentration and dispersion of travel demand in a day. A simulation-based methodology is proposed which involves multiplying the 96 samples by thousands of spectators. A number of profiles of hypothetical spectators were generated using the probability distribution of the sampled spectators. Another simulation model was developed to estimate the movement of spectators at a match held at a different venue. The probability distributions of the original samples were also used to generate spectator’ profiles. These profiles were then put into a railway network simulator that described the movement of spectators and the quality of railway services in a congested situation. Case studies in the Osaka Nagai Stadium were examined to evaluate the congestion reduction policies at railway stations near the stadium. Along with guiding spectators to the less congested stations, sub-attractions in the stadium after the game were also found to be effective in reducing peak time congestion at popular stations.

This paper narrates a history of developments in the traffic simulation models in Japan. Starting from 1971, basically two kinds of logic existed for reproducing dynamic traffic flow. These were the Block Density Method and the Input-Output Method. These methods were compared to the calculation engine of the network traffic simulation model. Subsequently, these methods evolved, were modified, and became more advanced as found in the AVENUE and SOUND simulation models. This development catered better to the changes in needs required by newer traffic simulation models and resulted in part from developments in computer technology. The first models do not include drivers’ route choice behaviour, but the later do. Initially these methods were applied to urban expressways; however, they later expanded to include surface street networks.

Probe vehicle data has great potential for improving the estimation accuracy of traffic situations. The primary objective of this study is to present a systematic method of estimating traffic states on an expressway by combining probe vehicle data with conventional fixed detector data, using a Kalman filter technique. This paper discusses the parameter of identification of simulation models used prior to the introduction of the new method. Then, the method to estimate traffic states using integrated data from probe vehicles and detectors is described. Experimental results showed that the estimation accuracy could be improved by the proposed method.

In order to evaluate ITS options and dynamic TDM options, it has become important to understand how to integrate traffic simulations and dynamic travel behaviour choice models. Although Traffic simulation is well organised, visual and understandable, it looks weak in regards to representing user behaviour. From the standpoint of travel behaviour analysis, this paper points out the consistency problems and specific points in the integration of traffic simulation and dynamic route choice model or dynamic time-of-day choice model. It is recommended that fundamental concepts, such as choice set and user segmentation be incorporated appropriately into traffic simulations.

The principle of driver’s route choice has long been the shortest path with a fixed time penalty of a toll. It is also understood that traffic is assigned on the road network based on user equilibrium with perfect information assumption. This paper demonstrates two empirical studies that pose questions to these traditional assumptions, to better understand route choice behavior. Multi-class user equilibrium assignment with imperfectly informed drivers’ classes is applied to a metropolitan area network first. Next, route choice behavior is directly observed and analyzed using probe’ car data.

The micro-simulator of individuals’ daily travel, PCATS, and a dynamic network simulator, DEBNetS, are integrated to form a simulation system for urban passenger travel. The components of the simulation system are briefly described, and three areas of on-going system improvement are described, i.e., (i) introduction of stochastic frontier models of prism vertex location, (ii) adoption of a fine grid system for quasi-continuous representation of space, and (iii) use of MCMC algorithms to handle colossal choice sets. Application case studies demonstrate that micro-simulation is a practical approach for demand forecasting and policy analysis, especially in the area of demand management.