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Abstract
System resources allocation optimization through dynamic scheduling is key to improving the service level of bike-sharing. This study innovatively introduces three types of invalid demand with negative effect including waiting, transfer, and abandoning, which consists of the total demand of bike-sharing system. Through exploring the dynamic relationship among users’ travel demands, the quantity and capacity of bikes at the rental points, the records of bicycles borrowed and returned, and the vehicle scheduling schemes, a demand forecasting model for bike-sharing is established. According to the predicted bikes and the maximum capacity limit at each rental point, an optimization model of scheduling scheme is proposed to reduce the invalid demand and the total scheduling time. A two-layer dynamic coupling model with iterative feedback is obtained by combining the demand prediction model and scheduling optimization model and is then solved by Nicked Pareto Genetic Algorithm (NPGA). The proposed model is applied to a case study and the optimal solution set and corresponding Pareto front are obtained. The invalid demand is greatly reduced from 1094 to 26 by an effective scheduling of 3 rounds and 96 minutes. Empirical results show that the proposed model is able to optimize the resource allocation of bike-sharing, significantly reduce the invalid demand caused by the absence of bikes at the rental point such as waiting in a place, walking to other rental points, and giving up for other travel modes, and effectively improve the system service level.
Highlights
With the popularization of the concept of sharing economy and green travel, bike-sharing, as an energy-saving, environmental protection, flexible, and healthy way of traveling, is being increasingly popular
Based on the theory of transportation travel, this paper focuses on the multi-period prediction of bicycle demand at each rental point and the formulation of dynamic scheduling scheme
E scheduling process is as follows: the command center generates a scheduling scheme according to the scheduling requirements of one or more rental points, which is executed by the scheduling vehicles. e vehicles start from the parking base, load and unload the bikes at multiple rental points according to the scheme, and return to the base
Summary
With the popularization of the concept of sharing economy and green travel, bike-sharing, as an energy-saving, environmental protection, flexible, and healthy way of traveling, is being increasingly popular. Shi et al formulated the VRP model for bike-sharing inventory rebalancing and vehicle routing and designed an improved particle swarm optimization (PSO) algorithm to solve this problem [7]. Dynamic scheduling of bikesharing is defined as vehicle routing problems with time window (VRPTW). E above literature mainly focused on the optimization method of path planning algorithm for scheduling vehicles, while most of them considered the demand for bike-sharing trips as static. Based on the traffic travel theory, it is the focus of this paper to predict the vehicle demand of each rental point over multiple periods and to formulate the dynamic scheduling scheme. Based on the theory of transportation travel, this paper focuses on the multi-period prediction of bicycle demand at each rental point and the formulation of dynamic scheduling scheme.
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