Using Simulation to Analyze Crowd Congestion and Mitigation at Canadian Subway Interchanges Case of Bloor–Yonge Station, Toronto, Ontario

With year after year of record ridership and demand only expected to grow, transit infrastructure is under increasing pressure. Examining the impact of the scheduling and coordination of subway lines at interchange stations is critical to reduce crowd congestion at station facilities. There is, however, a gap in knowledge concerning how crowd congestion is affected by the arrival patterns of trains. The effects of arrival patterns are especially critical at interchange stations where several train lines converge. A simulation-based analysis was performed to fill this knowledge gap. Field data were collected at the Bloor-Yonge Toronto Transit Commission subway station in Toronto, Ontario, Canada, a station known to be operating at capacity during peak periods. For performance of the analysis, a model of the station was developed, calibrated, and validated in the pedestrian simulator MassMotion. The congestion duration that passengers experienced was examined by varying the passenger volume and the arrival pattern of the two independent train lines. Adjusting the train arrival pattern was found to cause as much as a 63% reduction in the congestion passengers experienced. Additional scenarios were proposed as improvements over the status quo and tested for their significance in regard to improvement in congestion time experienced