Optimal Deployment of Electric Bicycle Sharing Stations: Model Formulation and Solution Technique
Zhiwei Chen (),
Yucong Hu (),
Jutint Li and
Xing Wu ()
Additional contact information
Zhiwei Chen: University of South Florida
Yucong Hu: South China University of Technology
Jutint Li: South China University of Technology
Xing Wu: Lamar University
Networks and Spatial Economics, 2020, vol. 20, issue 1, No 5, 99-136
Abstract This paper studies the problem of deploying electric bicycle (e-bike) sharing stations and determining their capacities, i.e. the number of shared e-bikes and charging piles, considering travelers’ responses to the charging demands and different deployment schemes. Given a one-way station-based setting, we propose an e-bike sharing network where the generalized trip cost is measured as the sum of the delay cost at stations and the travel time en-route. To estimate the trip costs, we modeled the pick-up and drop-off e-bikes at each sharing station as two different queues affected by e-bikes’ charging demands, and described the traffic flow of shared e-bike on each route based on Greenshield’s model. Further, the e-bike sharing station deployment problem was then formulated as a bi-level programming model, taking into account the government’s and individual travelers’ profits. The uniqueness of solution was proved. For the purpose of solution approach, this bi-level model was then reformulated into a single-level mixed-integer programming model, and a hybrid particle swarm optimization algorithm was proposed to solve the single-level model. Numerical experiments were presented to demonstrate the validity of the proposed model and solution technique. More importantly, through numerical experiments, further insights for designing an e-bike sharing system were examined and discussed: 1) sharing stations are bottlenecks in the e-bike sharing network, since the charging activities cause travelers large delay costs; 2) a well-designed quick-charging technology and reservation policy could be incorporated into e-bike sharing systems to reduce system costs; 3) the proposed hybrid particle swarm optimization algorithm shows good solution quality and convergence performance.
Keywords: Electric bicycle sharing; Network equilibrium; Bi-level programming; Hybrid particle swarm optimization (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s11067-019-09469-2 Abstract (text/html)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:kap:netspa:v:20:y:2020:i:1:d:10.1007_s11067-019-09469-2
Ordering information: This journal article can be ordered from
http://www.springer. ... ce/journal/11067/PS2
Access Statistics for this article
Networks and Spatial Economics is currently edited by Terry L. Friesz
More articles in Networks and Spatial Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().