When planning complex transit terminals, hubs, and stations, it is critical to analyze a facility’s capacity to handle expected passenger movements and volumes. In Toronto, Ontario, Canada, the revitalization of Union Station, the country’s busiest transit facility, involved the development of a set of high-fidelity pedestrian microsimulation models that were used to plan the improvement of this major intermodal hub. The pedestrian models were first created with MASSMOTION software, and construction plans and projected transit schedules were applied to represent various scenarios during and after the station revitalization. These models were then calibrated and validated against data from passenger counts and transit usage surveys to provide an accurate base for future scenarios. Through the use of a fully constructed model of the station with 2031 transit demand projections, a series of stress experiments was performed to evaluate the station’s capability to handle future passenger volumes. Under projected 2031 circumstances, it was found that Union Station could operate reasonably well. However, an increase in inbound transit passenger volumes of a mere 10% over 2031 projections could lead to severe levels of congestion within constrained sections of the station. This paper reports on the development of the microsimulation models and the model results for future scenarios.
Use of Agent-Based Crowd Simulation to Investigate the Performance of Large-Scale Intermodal Facilities: Case Study of Union Station in Toronto, Ontario, Canada
Transportation Research Record: Journal of the Transportation Research Board
Transportation Research Record: Journal of the Transportation Research Board ; 2540 , 1 ; 20-29
2016-01-01
Article (Journal)
Electronic Resource
English
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