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.

Transit operators are interested in strategies to improve service reliability as it is an important measure of performance and level of service. One of the common practices aimed at reducing service unreliability is holding control strategies. The design of these strategies involves the selection of a set of time point stops and the holding criteria for regulating the departure time. The interactions between passenger activity, transit operations, and traffic dynamics must be dynamically modeled to analyze the impacts of holding strategies on transit performance. An evaluation of different holding criteria and the number and location of time point stops was conducted with BusMezzo, a dynamic transit simulation model. The holding strategies were implemented in the model and applied to a high-frequency trunk bus line in Stockholm, Sweden. The analysis of the results considers the implications of holding strategies from both passenger and operator perspectives. The analysis suggests substantial gains are possible by implementing a holding strategy on the basis of the mean headway from the preceding and the succeeding buses. This strategy is the most efficient for passenger time savings as well as fleet costs and crew management.

This paper presents a transit simulation model designed to support evaluation of operations, planning and control, especially in the context of Advanced Public Transportation Systems (APTS). Examples of potential applications include frequency determination, evaluation of real-time control strategies for schedule maintenance and assessing the effects of vehicle scheduling on the level of service. Unlike most previous efforts in this area, the simulation model is built on a platform of a mesoscopic traffic simulation model, which allows modeling of the operation dynamics of large-scale transit systems taking into account the stochasticity due to interactions with road traffic. The capabilities of Mezzo as an evaluation tool of transit operations are demonstrated with an application to a realworld high-demand bus line in the Tel Aviv metropolitan area under various scenarios. The headway distributions at two stops are compared with field observations and show good consistency between simulated and observed data.

Real-time information (RTI) is increasingly being implemented in transit networks worldwide. The evaluation of the effect of RTI requires dynamic modeling of transit operations and of passenger path choices. The authors present a dynamic transit analysis and evaluation tool that represents timetables, operation strategies, RTI, adaptive passenger choices, and traffic dynamics at the network level. Transit path choices are modeled as a sequence of boarding, walking, and alighting decisions that passengers undertake when carrying out their journey. The model was applied to the Metro network area of Stockholm, Sweden, under various operating conditions and information provision scenarios, as a proof of concept. An analysis of results indicated substantial path choice shifts and potential time savings associated with more comprehensive RTI provision and transfer coordination improvements.

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.

Analysis of public transport system performance and level of service in urban areas is essential. Dynamic modeling of traffic conditions, passenger demand, and transit operations is important to represent adequately the complexity of and the interactions between these components in modern public transportation systems. This paper presents a transit simulation model designed to support evaluation of operations planning and control, especially in the context of advanced public transportation systems. Unlike most previous efforts in this area, the simulation model is built on a platform of a mesoscopic traffic simulation model, which allows modeling of the operation dynamics of large-scale transit systems, taking into account the main sources of service uncertainty and stochasticity. The capabilities of Mezzo as an evaluation tool of transit operations are demonstrated with an application to a real-world, high-demand bus line in metropolitan Tel Aviv, Israel, under various scenarios. The application shows that important phenomena such as bus bunching are reproduced realistically. A comparison of simulated running times and headway distributions with field data shows the model is capable of replicating observed data.

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.

Transit operations involve several inherent sources of uncertainty, including dispatch time from the origin terminal, travel time between stops, and dwell time at stops. Bus-holding control strategies are a prominent method applied by transit operators to improve transit performance and level of service. The common practice is to regulate departures from a limited number of stops by holding buses until their scheduled departure time. An analysis of the performance of a high-frequency bus line in Stockholm, Sweden, based on automatic vehicle location data showed that this control strategy was not effective in improving service regularity along the line. The analysis also indicated that drivers adjusted their speed according to performance objectives. Implications of a control strategy that regulates departures from all stops on the basis of the headways of the preceding bus and the following bus were evaluated with BusMezzo, a transit operations simulation model. The results suggest that this strategy can improve service performance considerably from both passengers' and operator's perspectives. In addition, the strategy implies cooperative operations, as the decisions of each driver are interdependent with other drivers' decisions, and mutual corrections can be made. Difficulties in realizing the benefits of the proposed strategy in practice, such as dispatching from the origin terminal, driver scheduling, and compliance, are discussed. The implications of several practical considerations are assessed by conducting a sensitivity analysis as part of the preparations for a field experiment designed to test the proposed control strategy.