A Locational Demand Model for Bike-Sharing

Ang Xu (), Chiwei Yan (), Chong Yang Goh () and Patrick Jaillet ()
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Ang Xu: Department of Industrial Engineering and Operations Research, University of California, Berkeley, California 94720
Chiwei Yan: Department of Industrial Engineering and Operations Research, University of California, Berkeley, California 94720
Chong Yang Goh: Uber Technologies, Inc., San Francisco, California 94158
Patrick Jaillet: Department of Electrical Engineering and Computer Science and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Manufacturing & Service Operations Management, 2025, vol. 27, issue 3, 897-916

Abstract: Problem definition : Micromobility systems (bike-sharing or scooter-sharing) have been widely adopted across the globe as a sustainable mode of urban transportation. To efficiently plan, operate, and monitor such systems, it is crucial to understand the underlying rider demand—where riders come from and the rates of arrivals into the service area. They serve as key inputs for downstream decisions, including capacity planning, location optimization, and rebalancing. Estimating rider demand is nontrivial as most systems only keep track of trip data, which are a biased representation of the underlying demand. Methodology/results : We develop a locational demand model to estimate rider demand only using trip and vehicle status data. We establish conditions under which our model is identifiable. In addition, we devise an expectation-maximization (EM) algorithm for efficient estimation with closed-form updates on location weights. To scale the estimation procedures, this EM algorithm is complemented with a location-discovery procedure that gradually adds new locations in the service region with large improvements to the likelihood. Experiments using both synthetic data and real data from a dockless bike-sharing system in the Seattle area demonstrate the accuracy and scalability of the model and its estimation algorithm. Managerial implications : Our theoretical results shed light on the quality of the estimates and guide the practical usage of this locational demand model. The model and its estimation algorithm equip municipal agencies and fleet operators with tools to effectively monitor service levels using daily operational data and assess demand shifts because of capacity changes at specific locations.

Keywords: locational demand model; bike-sharing; expectation-maximization; location discovery (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:27:y:2025:i:3:p:897-916

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