Issues and Strategies Involved in Developing Agent-Based Multimodal Network Simulation Model for Transportation Planning: Lessons from a Case Study on the Greater Toronto and Hamilton Area

摘要

The paper presents the issues and strategies involved in developing an agent-based multimodal network simulation model for the Greater Toronto and Hamilton Area (GTHA). The model was developed by using a Java-based open source simulation platform: MATSim. The issues and strategies presented utilize a geocoded automobile network and General Transit Feed Specification (GTFS) data of multiple transit agencies within the study area. While network simulation model for automobile network is common, an integrated multimodal network that combines auto and transit network (physical network and daily transit schedules) has not been developed for large study area, such as the GTHA by anyone. A key challenge is to integrate the GTFS data seamlessly in the multimodal framework. The GTFS data allowed meshing the auto network and the transit network together, creating a fully functioning multimodal network. The main challenge associated with this task is the determination of network resolution. The auto network is at times at too low of a resolution relative to the transit network, while the transit network often contained too much detail to be relevant for traffic simulation for a region as large as the GTHA. The paper presents guidelines and example of resolving these issues and overcoming the challenges.