Bike-Sharing Static Rebalancing by Considering the Collection of Bicycles in Need of Repair

Bike-sharing systems, which are used in many cities worldwide, need to maintain a balance between the availability of bicycles and the availability of unoccupied bicycle slots. This paper presents an investigation of the net flow of each bike-sharing station in Jersey City. The data was recorded at...

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Bibliographic Details
Published in:Journal of advanced transportation 2018-01, Vol.2018 (2018), p.1-18
Main Authors: Zhang, Sheng, Huang, Zhongxiang, Xiang, Guanhua
Format: Article
Language:English
Subjects:
Algorithms
Autoregressive models
Availability
Bicycles
Case studies
Demand
Global optimization
Integer programming
Linear programming
Logistics
Maintenance and repair
Management science
Mathematical optimization
Methods
Particle swarm optimization
Repair
Researchers
Transportation
Travel
Traveling salesman problem
Vehicles
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Summary:Bike-sharing systems, which are used in many cities worldwide, need to maintain a balance between the availability of bicycles and the availability of unoccupied bicycle slots. This paper presents an investigation of the net flow of each bike-sharing station in Jersey City. The data was recorded at 1-minute intervals. The sum of the initial bicycle number and the minimum net flow value was determined to be the demand for static rebalancing, and this led to the proposal of a bike-sharing demand prediction method based on autoregressive integrated moving average models. Considering that the existence of bicycles in a state of disrepair may adversely affect demand prediction and routine planning, we present an integer linear programming formulation to model bike-sharing static rebalancing. The proposed formulation takes into account the problem introduced by the need to collect bicycles in need of repair. A hybrid Discrete Particle Swarm Optimization (DPSO) algorithm was proposed to solve the model, which incorporates a reduced variable neighborhood search (RVNS) functionality together with DPSO to improve the global optimization performance. The effectiveness of the algorithms was verified by a detailed numerical example.
ISSN:0197-6729
2042-3195
DOI:10.1155/2018/8086378