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

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成果类型:
期刊论文
作者:
Zhang, Sheng;Xiang, Guanhua;Huang, Zhongxiang*
通讯作者:
Huang, Zhongxiang
作者机构:
[Huang, Zhongxiang; Zhang, Sheng; Xiang, Guanhua] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha 410114, Hunan, Peoples R China.
通讯机构:
[Huang, Zhongxiang] C
Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha 410114, Hunan, Peoples R China.
语种:
英文
关键词:
Global optimization;Integer programming;Particle swarm optimization (PSO);Repair;Sporting goods;Autoregressive integrated moving average models;Demand prediction;Discrete particle swarm optimization;Integer linear programming formulation;Rebalancing;Sharing systems;Variable neighborhood search;Bicycles
期刊:
Journal of Advanced Transportation
ISSN:
0197-6729
年:
2018
卷:
2018
页码:
1-18
基金类别:
This research has been substantially supported by research grants from the National Natural Science Foundation Council of China (51408058, 51338002), grants from the China Scholarship Council (201608430161), and Open Fund of Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety (Changsha University of Science & Technology), Ministry of Education, under grant number kfj 130301.
机构署名:
本校为第一且通讯机构
院系归属:
交通运输工程学院
摘要:
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 integrate...

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