Network Rebalance and Operational Efficiency of Sharing Transportation System: Multi-Objective Optimization and Model Predictive Control Approaches

Abstract

Sharing transportation systems can significantly promote travelers convenience and efficiency. As a vital part, bike-sharing system (BSS) has effectively solved “the-last-mile” problem during transportation interchange, but bike imbalance between docks severely deteriorates operational efficiency of BSS. In this paper, we present multi-objective optimization and predictive control approaches to tackle the bike rebalancing problem, where optimal redistributing strategies can maximize the operational efficiency of BSS with respects to equilibrium state and redistribution cost. A dock-based BSS dynamic network is modeled based on a proximity graph, in which connection relation, bike usage, and redistribution flow are formulated. To measure the operational efficiency, a performance metric is presented to consider both benefits of user and operator. To satisfy bike renting, returning, and redistributing requirements, model predictive control (MPC) is then employed to compute feasible and optimal redistribution strategies based on the network model. The effectiveness of multi-objective optimization and MPC is verified on different topologies of BSS. Experimental results show that when the BSS reaches the equilibrium state, the operational efficiency will be maximized in the proposed approaches.