TY - JOUR
T1 - Rebalancing stochastic demands for bike-sharing networks with multi-scenario characteristics
AU - Ma, Guanhua
AU - Zhang, Bowen
AU - Shang, Changjing
AU - Shen, Qiang
N1 - Funding Information:
This work was partly supported by the National Natural Science Foundation of China (No. 71722007, 71931001), and partly by a Sr Cymru II COFUND Fellowship, UK.
Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2021/4/1
Y1 - 2021/4/1
N2 - Bike-sharing networks have become a carbon-emission and environmentally friendly form of transportation in recent years. However, the asymmetric demand patterns of user behaviour, both temporally and spatially, inevitably lead to an imbalance in the distribution of shared bikes in cities, thereby becoming the greatest obstacle to the networks’ development. Based on the real-world data of cycling trips, we analyse the challenging problem of imbalanced bike distribution from the entire-city perspective, establishing that the static rebalancing demand for the whole city is a stochastic variable with multi-scenario characteristics. On this basis, we develop an integer programming model to consider multiple rebalancing vehicles with time-varying rental costs, to alleviate the imbalanced bike distribution, while also analysing the intrinsic properties of such a model. We further propose a chance constraint programming model, optimising a bike-sharing network through the implementation of various genetic algorithms that employ block crossover and variable mutation operators. We reveal the inability of deterministic models in addressing the real-world problem of rebalancing demands for operational bike-sharing. In the meantime, supported with stochastic simulation, we demonstrate that the proposed approach can resolve this problem both effectively and efficiently, ensuring the delivery of a high-level bike-sharing service across an entire metropolitan city.
AB - Bike-sharing networks have become a carbon-emission and environmentally friendly form of transportation in recent years. However, the asymmetric demand patterns of user behaviour, both temporally and spatially, inevitably lead to an imbalance in the distribution of shared bikes in cities, thereby becoming the greatest obstacle to the networks’ development. Based on the real-world data of cycling trips, we analyse the challenging problem of imbalanced bike distribution from the entire-city perspective, establishing that the static rebalancing demand for the whole city is a stochastic variable with multi-scenario characteristics. On this basis, we develop an integer programming model to consider multiple rebalancing vehicles with time-varying rental costs, to alleviate the imbalanced bike distribution, while also analysing the intrinsic properties of such a model. We further propose a chance constraint programming model, optimising a bike-sharing network through the implementation of various genetic algorithms that employ block crossover and variable mutation operators. We reveal the inability of deterministic models in addressing the real-world problem of rebalancing demands for operational bike-sharing. In the meantime, supported with stochastic simulation, we demonstrate that the proposed approach can resolve this problem both effectively and efficiently, ensuring the delivery of a high-level bike-sharing service across an entire metropolitan city.
KW - Bike sharing network
KW - Genetic algorithms
KW - Integer programming model
KW - Static rebalancing operations
UR - http://www.scopus.com/inward/record.url?scp=85098939342&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2020.12.044
DO - 10.1016/j.ins.2020.12.044
M3 - Article
SN - 0020-0255
VL - 554
SP - 177
EP - 197
JO - Information Sciences
JF - Information Sciences
ER -