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The performance of urban transportation systems can be improved if travelers make better-informed decisions using advanced modeling techniques. However, modeling city-level transportation systems is challenging not only because of the network scale but also because they encompass multiple transportation modes. This paper introduces a novel simulation framework that efficiently supports large-scale agent-based multi-modal transportation system modeling. The proposed framework utilizes both microscopic and mesoscopic modeling techniques to take advantage of the strengths of each modeling approach. In order to increase the model scalability, decrease the complexity and achieve a reasonable simulation speed, the proposed framework utilizes parallel simulation through two partitioning techniques: spatial partitioning by separating the network geographically and vertical partitioning by separating the network by transportation mode for modes that interact minimally. The proposed framework creates multi-modal plans for each trip and tracks the travelers trips on a second-by-second basis across the different modes. We instantiate this framework in a system model of Los Angeles (LA) supporting our study of the impact on transportation decisions over a 5 hour period of the morning commute (7am-12pm). The results show that by modifying travel choices of only 10% of the trips a significant reduction in traffic congestion is achievable that results in better traffic flow and lower travel times.

Large-scale Agent-based Multi-modal Modeling of Transportation Networks - System Model and Preliminary Results