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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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This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity.

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