Abstract
Bike Share Toronto is a docked bike share system (“System”) that operates within the City of Toronto. It began operating in 2011 and has expanded to include 625 stations as of June 2022. This paper uses a microsimulation model of the System to examine the operational challenge of rebalancing bike share networks. Using simulations of the System’s operation each day in 2021, this paper compares the impact of three different rebalancing scenarios upon rebalancing operations in the system.
The three scenarios cover: (a) the “as is” condition using observed rebalancing operations; (b) a worst-case scenario where no rebalancing operations are conducted, and (c) an optimized scenario where rebalancing operations are planned with perfect knowledge of ridership patterns. The optimized scenario offers a theoretical maximum efficiency to better understand how operations could be improved.
The results of the model’s analysis show that the number and length of delays where a user must relocate to another station due full or empty stations decrease dramatically between the worst-case scenario and the “as is” scenario. Under the optimized scenario, users experience fewer delays and the tour lengths of the trucks performing rebalancing operations are 36% lower than in the “as is” scenario. These results highlight the potential for improved forecasting, route planning and rebalancing to reduce the System’s operating costs and improve user experience.
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