Dynamic Scheduling Based on Predicted Inventory Variation Rate for Public Bicycle System

Liang Gao,Wei Xu,Yifeng Duan

doi:10.3390/su11071885

Abstract

To improve efficiency and reduce the total scheduling cost of the public bicycle system (PBS), dynamic scheduling based on the predicted inventory variation rate (DS-PIVR) is proposed. Regarding a station in the PBS as an inventory system, its inventory variation rate during the scheduling period and its inventory rate at the end of the scheduling period were predicted based on the stationary Markov process condition. A mixed integer programming (MIP) model, whose objective is to minimize the total scheduling distance, was established to describe the dynamic scheduling problem (DSP). Results from Boston and Washington D.C. PBSs show that, when compared to the dynamic scheduling based on the rolling horizon (DS-RH), the DS-PIVR method could at most shorten the routing distance by 62.25% (for Boston) and 74.7% (for Washington D.C.) among all scheduling areas, and could at most shorten the total routing distance for the whole PBS by 21.06% (for Boston) and 17.26% (for Washington D.C.). Moreover, the DS-PIVR method makes the repositioning vehicle journey only once and keeps the inventory rate of each station in balance during the scheduling period. Furthermore, the DS-PIVR method provides a promising reference to improve the operation efficiency by reducing the scheduling cost and the quality of service by satisfying the users’ demand in time during the rush hours for the PBS operators.

Highlights

As a short-distance, carbon-free traffic mode, public bicycles can solve the “last mile” problem of urban public transportation
This paper proposes a dynamic scheduling method for the public bicycle system (PBS) based on the predicted inventory variation rate
This paper proposes a dynamic scheduling method based on the predicted inventory variation rate for the rebalancing of a PBS

Summary

Introduction

As a short-distance, carbon-free traffic mode, public bicycles can solve the “last mile” problem of urban public transportation. Studies on PBSs emerge [3,4], including bicycle safety [1] and health [5], modal shift [2], usage patterns [1,4,6], spatial-temporal features of the operations [5,7,8,9], etc. The public bicycle systems (PBSs) can be mainly divided into docking and dockless PBSs [10]. The former provides services via docking stations, while the latter realizes the standardized management by setting up electronic fences [10]. Scheduling the vehicles to rebalance the system is the key to solving this problem [6,12]

Methods

Findings

Discussion

Conclusion