Abstract
In Bike-Sharing System (BSS), the initial number of bikes at station will affect the time interval and the amount of rebalancing, which is usually empirically determined and does not reflect the characteristics of consumer demand in finer time granularity, thus possibly leading to biased conclusions. In this paper, a fleet allocation method considering demand gap is first proposed to calculate the initial number of bikes at each station. Then, taking the number of demand gap periods as the decision variable, an optimization model is built to minimize the total rebalancing amount. Furthermore, the research periods are divided into multiple subcycles, the single-cycle and multicycle rebalancing strategies are presented, and the additional subcycle rebalancing method is introduced to amend the number of bikes between subcycles to decrease the rebalancing amount of the next subcycle. Finally, our methods are verified in effectively decreasing the rebalancing amount in a long-term rebalancing problem.
Highlights
In recent years, the rapid expansion of motorized transportation system in cities has made the urban environment deteriorate rapidly and traffic congestion seriously, posing a serious threat to the health and travel convenience of urban residents [1]
According to equations (2) and (3), the initial number of bikes is calculated by the demand gap periods optimization (DGPO). en, perform rebalancing by sing-cycle rebalancing strategy (SCRS) or multicycle rebalancing strategy (MCRS)
Of these results, regarding 14 days as a subcycle and performing MCRS yield the best result. Both SCRS and MCRS have significantly reduced the rebalancing amount and the latter is superior in situation of excessive periods. Another issue deserving special attention is that additional subcycle rebalancing method (ACRM) is modified to rebalance targeting only those stations with a rebalancing amount greater than the threshold value 40, namely, MMCRS. e validity of MMCRS and MCRS is verified by experiments. e data of 28 days are still selected as experiment data, which is divided into four cycles with 7 days for each subcycle, and the initial number of bikes of each subcycle is calculated based on DGPO. e compared experiment results are shown in Table 6 and Figure 8
Summary
The rapid expansion of motorized transportation system in cities has made the urban environment deteriorate rapidly and traffic congestion seriously, posing a serious threat to the health and travel convenience of urban residents [1]. Most of them are intended to minimize the total rebalancing cost or time from the operator’ s point of view to improve the effectiveness and efficiency of BSS [10, 11] In addition to these objectives, many of the literature target customer satisfaction or service level [12]. Sayarshad et al [15] proposed a mathematical model which attempted to optimize a BSS by determining the minimum required bike fleet size in order to minimize unmet demand, unutilized bikes, and the need to transport empty bikes between rental stations. Modeled the evolution of the number of vehicles at each station as a stochastic process and proposed a rebalancing strategy iteratively to solve a chance-constrained optimization problem in order to find a rebalancing schedule ensuring no service failures in the future with a given level of confidence. (iii) Based on cycle division, a multicycle rebalancing strategy (MCRS) is presented, including a sing-cycle rebalancing strategy (SCRS) and an additional subcycle rebalancing method (ACRM). e cycle division method can give full play to the effect of the fleet allocation method in reducing the rebalancing amount
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
