Optimizing bike rebalancing strategies in free-floating bike-sharing systems: An enhanced distributionally robust approach

Chen, Qingxin1,2; Ma, Shoufeng1,2; Li, Hongming3; Zhu, Ning1; He, Qiao-Chu4

2024-04-01

10.1016/j.tre.2024.103477

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW   影响因子和分区

1366-5545

1878-5794

Bike -sharing systems are important components of urban transportation systems that facilitate short -distance travel. In recent years, free-floating bike -sharing systems have become popular. However, a major problem in such systems is the increasing number of idle bikes due to their spatial dispersion, which leads to a loss of demand and difficulty in bike rebalancing. To overcome these drawbacks, operators reserve parking areas, and laborers are incorporated into the bike rebalancing operation. We propose a novel dynamic bike rebalancing strategy based on trucks and laborers for free-floating bike -sharing systems. This strategy combines bike collection by laborers and bike transportation by trucks for rebalancing operations. In addition, we consider broken bike detection by laborers and user dynamics in two extension models. Because of the demand uncertainty in bike -sharing systems, we employ an enhanced distributionally robust optimization (EDRO) approach to protect against uncertainty. A targetlevel constraint is added to the general distributionally robust optimization (DRO) model to further improve the out -of -sample performance. An adaptive robust strategy is also employed to avoid overly conservative solutions. Furthermore, a linear decision rule is adopted to reformulate the EDRO model into a mixed -integer programming model, which guarantees computational tractability. The results of numerical experiments show that the EDRO model outperforms other benchmark models on a series of measures. Moreover, we verify the value of laborers in dynamic rebalancing operations and explore the effect of different bike supply levels and several other parameters on the value of laborers. We also test how the target -level parameter affects the model performance.

Dynamic bike rebalancing Free-floating bike-sharing system Value of laborers Enhanced distributionally robust optimization Linear decision rule

SCI ; SSCI ; EI

英语

其他

National Natural Science Foundation of China["72010107004","72122015","71971154","72071101"]

Business & Economics ; Engineering ; Operations Research & Management Science ; Transportation

Economics ; Engineering, Civil ; Operations Research & Management Science ; Transportation ; Transportation Science & Technology

WOS:001221042700001

PERGAMON-ELSEVIER SCIENCE LTD

ENGINEERING

Web of Science

1.Tianjin Univ, Inst Syst Engn, Coll Management & Econ, Tianjin 300072, Peoples R China
2.Tianjin Univ, Lab Computat & Analyt Complex Management Syst CACM, Tianjin 300072, Peoples R China
3.Army Mil Transportat Univ, Tianjin 300161, Peoples R China
4.Southern Univ Sci & Technol, Sch Business, Shenzhen 518055, Peoples R China

Chen, Qingxin,Ma, Shoufeng,Li, Hongming,et al. Optimizing bike rebalancing strategies in free-floating bike-sharing systems: An enhanced distributionally robust approach[J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW,2024,184.

Chen, Qingxin,Ma, Shoufeng,Li, Hongming,Zhu, Ning,&He, Qiao-Chu.(2024).Optimizing bike rebalancing strategies in free-floating bike-sharing systems: An enhanced distributionally robust approach.TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW,184.

Chen, Qingxin,et al."Optimizing bike rebalancing strategies in free-floating bike-sharing systems: An enhanced distributionally robust approach".TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 184(2024).