The activity-based model system is being coined as the next-generation demand-forecasting model. The agent-based transport simulation toolkit MATSIM is a fully integrated system that models decisions from the long term to the short term, and these decisions in MATSIM are activity-based models. This paper describes the application of MATSIM in a large-scale multiagent-based transport simulation for Shanghai, China. First, algorithms for integrating new data in Shanghai with MATSIM inputs such as synthetic population, facilities, and network are separately designed according to data characteristics. Then activity-based modeling is introduced to generate population plans, and activity replanning is employed to learn the better travel plans; a utility-based approach is used to model scoring for a plan. Finally, a full MATSIM-based simulation platform for the Shanghai scenario is built in detail. The scenario contains 200,000 synthetic persons simulated on a network with 50,000 links. The relaxed state of the simulation system is reached after 100 iterations of replanning procedures, and the mode choice, route choice, and activity time allocation modules are used to optimize agents’ activity plans. The feasibility of transport simulation in Shanghai by MATSIM is validated against the mode split and the observed counts. Extensive simulation results for the designed Shanghai simulation scenarios indicate that most of the observed counts match quite well with the traffic simulation volumes and demonstrate the potential of MATSIM for large-scale dynamic transport simulation.

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In domestic major cities, the development of Urban Expressway is network-oriented. The traffic flow forecasting system is the important prerequisite and foundation of realizing real-time traffic management and control. However, the traffic flow forecasting research is mainly based on highways. Research and application of short-term traffic forecasting for urban expressway is severely insufficient. Therefore, the study of urban expressway flow forecasting is discussed and a short-term traffic flow forecasting system for urban expressway based on k-NN nonparametric regression model is proposed in this study. First, the study analyze the characteristics and needs of the urban expressway traffic flow, introduce k-NN nonparametric regression model and design the short-term traffic flow forecasting system based on k-NN overall. Then, the short-term urban expressway flow forecasting system based on k-NN is established in three aspects: the historical database, the search mechanism and algorithm parameters, and the forecasting plan. At last, a short-term traffic forecasting for urban expressway based on k-NN nonparametric regression model is developed in the VS2010 VC++ platform. Utilizing the Shanghai urban expressway section measured traffic flow data, the comparison of average and weighted k-NN nonparametric regression model is discussed and the reliability of the forecasting result is analyzed. Results show that the accuracy of the proposed method, under the 5-minute interval, is over 90 percent, which best proves the reasonableness of the proposed forecasting model based on k-NN nonparametric model.

This paper proposes the design and Paramics-based evaluation of a two stage fuzzy logic traffic signal controller (TSTFC) for an isolated intersection. The fuzzy controller employs traffic intensity-based two stage fuzzy logic procedure, and the design principles of two-stage fuzzy controller are first presented. Then, the COM-based hybrid programming is employed to make the micro traffic simulator Paramics interact with fuzzy controllers developed by Matlab, and a Paramics-based simulation platform of TSTFC is developed via Paramics API. Finally, experiments are conducted on a typical urban isolated intersection and the performance of the developed two-stage fuzzy control simulation model is validated by comparison to those of fixed-time, actuated, and classic fuzzy controllers for different traffic conditions. Extensive simulation results have demonstrated the potential of developed hybrid programming in time efficiency and simulating real traffic conditions, and indicated that the signal strategy derived from TSTFC is more effective when traffic status at intersections is above low saturation.

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