Traffic simulation is an important tool for modeling the operations of dynamic traffic systems. Although microscopic simulation models provide a detailed representation of the traffic process, macroscopic and mesoscopic models capture the traffic dynamics of large networks in less detail but without the problems of application and calibration of microscopic models. This paper presents a hybrid mesoscopic–microscopic model that applies microscopic simulation to areas of specific interest while simulating a large surrounding network in less detail with a mesoscopic model. The requirements that are important for a hybrid model to be consistent across the models at different levels of detail are identified. These requirements vary from the network and route choice consistency to the consistency of the traffic dynamics at the boundaries of the microscopic and mesoscopic submodels. An integration framework that satisfies these requirements is proposed. A prototype hybrid model is used to demonstrate the application of the integration framework and the solution of the various integration issues. The hybrid model integrates MITSIMLab, a microscopic traffic simulation model, and Mezzo, a newly developed mesoscopic model. The hybrid model is applied in two case studies. The results are promising and support both the proposed architecture and the importance of integrating microscopic and mesoscopic models.

Abstract-This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-behavior data, mainly carfollowing and lane-changing patterns, on Swedish roads. To eliminate the measurement noise in acquired car-following patterns, the Kalman smoothing algorithm was applied to the state-space model of the physical states (acceleration, speed, and position) of both instrumented and tracked vehicles. The denoised driving patterns were used in the analysis of driver properties in the car-following stage. For further modeling of car-following behavior, we developed and implemented a consolidated fuzzy clustering algorithm to classify different car-following regimes from the preprocessed data. The algorithm considers time continuity of collected driver-behavior patterns and can be more reliably applied in the classification of continuous car-following regimes when the classical fuzzy C-means algorithm gives unclear results.

The calibration and validation approach and results from a case study applying the microscopic traffic simulation tool MITSIMLab to a mixed urban-freeway network in the Brunnsviken area in the north of Stockholm, Sweden, under congested traffic conditions are described. Two important components of the simulator were calibrated: driving behavior models and travel behavior components, including origin–destination flows and the route choice model. In the absence of detailed data, only aggregate data (i.e., speed and flow measurements at sensor locations) were available for calibration. Aggregate calibration uses simulation output, which is a result of the interaction among all components of the simulator. Therefore, it is, in general, impossible to identify the effect of individual models on traffic flow when using aggregate data. The calibration approach used takes these interactions into account by iteratively calibrating the different components to minimize the deviation between observed and simulated measurements. The calibrated MITSIMLab model was validated by comparing observed and simulated measurements: traffic flows at sensor locations, point-to-point travel times, and queue lengths. A second set of measurements, taken a year after the ones used for calibration, was used at this stage. Results of the validation are presented. Practical difficulties and limitations that may arise with application of the calibration and validation approach are discussed.

The paper presents a mesoscopic traffic simulation model, particularly suited for the development of integrated meso-micro traffic simulation models. The model combines a number of the recent advances in simulation modeling, such as discrete-event time resolution and combined queue-server and speed-density modeling, with a number of new features such as the ability to integrate with microscopic models to create hybrid traffic simulation. The ability to integrate with microscopic models extends the area of use to include evaluation of ITS systems, which often require the detailed modeling of vehicles in areas of interest, combined with a more general modeling of large surrounding areas to capture network effects of local phenomena. The paper discusses the structure of the model, presents a framework for integration with micro models, and illustrates its validity through a case study with a congested network north of Stockholm. It also compares its performance with a hybrid model applied to the same network.

Personal rapid transit (PRT) accommodates groups of passengers that request transport to a desired destination. Empty vehicles need to be moved to stations with waiting passengers or with expected demand. Efficient redistribution of empty vehicles is critical in larger PRT networks, which have running times longer than acceptable waiting times. Previous studies have developed methods for allocating destinations to empty PRT vehicles sequentially as they become available. This study refines the allocation of empty vehicles into three stages. The first stage is essentially the previous sequential allocation. In the second and third stages, empty vehicles en route are reallocated by switching destinations. In the second stage, waiting passengers are allocated the nearest vehicle on the basis of waiting time. In the third stage, the remaining empty vehicles en route are reallocated on the basis of minimum running distance. The optimization problem in the third stage corresponds to the transportation problem, and it is solved approximately by a heuristic method suggested by Russel. Applied to a network with 25 stations, 16 km of guideway, and 200 vehicles, the combined reallocation methods reduced average waiting and the longest wait to about half compared with sequential decisions alone.