A Locational Demand Model for Bike-Sharing

50 Pages Posted: 8 Jan 2019 Last revised: 10 Jan 2025

See all articles by Ang Xu

Ang Xu

University of California, Berkeley

Chiwei Yan

University of California, Berkeley

Chong Yang Goh

Massachusetts Institute of Technology (MIT)

Patrick Jaillet

Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science

Date Written: May 18, 2023

Abstract

Problem Definition: Micro-mobility 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 is 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 due to capacity changes at specific locations.

Keywords: locational demand model, bike-sharing, expectation-maximization, location discovery

Suggested Citation

Xu, Ang and Yan, Chiwei and Goh, Chong Yang and Jaillet, Patrick, A Locational Demand Model for Bike-Sharing (May 18, 2023). Available at SSRN: https://ssrn.com/abstract=3311371 or http://dx.doi.org/10.2139/ssrn.3311371

Ang Xu

University of California, Berkeley ( email )

310 Barrows Hall
Berkeley, CA 94720
United States

Chiwei Yan (Contact Author)

University of California, Berkeley ( email )

4141 Etcheverry Hall
Berkeley, CA 94720-1777
United States

Chong Yang Goh

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Patrick Jaillet

Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science ( email )

77 Massachusetts Avenue
Cambridge, MA 02139-4307
United States

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