A static bike repositioning model in a hub-and-spoke network framework

Di Huang, Xinyuan Chen, Zhiyuan Liu, Cheng Lyu, Shuaian Wang, Xuewu Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)

Abstract

This paper addresses a static bike repositioning problem by embedding a short-term demand forecasting process, the Random Forest (RF) model, to account for the demand dynamics in the daytime. To tackle the heterogeneous repositioning fleets, a novel repositioning operation strategy constructed on the hub-and-spoke network framework is proposed. The repositioning optimization model is formulated using mixed-integer programming. An artificial bee colony algorithm, integrated with a commercial solver, is applied to address computational complexity. Experimental results show that the RF can achieve a high forecasting accuracy, and the proposed repositioning strategy can efficiently decrease the users’ dissatisfaction.

Original languageEnglish
Article number102031
JournalTransportation Research Part E: Logistics and Transportation Review
Volume141
DOIs
Publication statusPublished - Sep 2020

Keywords

  • Bike repositioning
  • Demand forecasting
  • Hub-and-spoke network framework
  • Hub-first-route-second
  • Random forests

ASJC Scopus subject areas

  • Business and International Management
  • Civil and Structural Engineering
  • Transportation

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