A Hyper-heuristic Approach to the Strategic Planning of Bike-Sharing Infrastructure

Rong Cheng, Shaopeng Zhong, Zhong Wang, Otto Anker Nielsen, Yu Jiang

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Abstract

With the increasing concern on carbon emission, climate change, and human well-being, governments worldwide are exploring ways to encourage the usage of sustainable modes of transport. Particularly, cycling is gaining attention as a healthy and green travel mode, and bicycle-sharing systems are experiencing world-spread adoption. Moreover, in response to the COVID-19 pandemic, countries have begun to expand cycling infrastructures to promote cycling considering its advantages of keeping proper social distance. This study thus develops a bi-level model for the strategic planning of the infrastructure for a bike-sharing system. The upper-level problem is to simultaneously determine the locations of bike stations and lanes to minimize the sum of the construction cost and total travelers’ travel time cost. The lower-level problem is the combined mode and route choice network equilibrium problem with elastic cycling demand. One of the novelties of this study to the existing bike network literature is that it captures the reality that some travelers only begin to cycle and use bike-sharing services when there are bike stations close to both their origins and destinations. To solve the proposed bi-level model, a sequence-based selection hyper-heuristic is developed, which employs a hidden Markov model as the online learning method to determine a set of problem-tailored heuristics to explore the solution space. Numerical examples are carried out to examine the model properties and algorithm performance. The results demonstrate the positive impact of bike infrastructures on promoting cycling measured by the mode share increment.
Original languageEnglish
Article number108704
JournalComputers & Industrial Engineering
Volume173
Number of pages35
ISSN0360-8352
DOIs
Publication statusPublished - 2022

Keywords

  • Multimodal transportation
  • Bicycle network design
  • Bi-level programming
  • Hyper-heuristic

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