Incentive-Based Simulation-Optimization Framework for Rebalancing Micromobility Systems

Abstract

This study addresses the critical challenge of rebalancing electric scooters in rapidly expanding bike-sharing systems, driven by fluctuating demand. An innovative user-adaptive rebalancing strategy is introduced that motivates users to alter their destinations toward high-demand areas, specifically through user-centered incentives for destination and charging zone relocations. The strategy aims to streamline rebalancing operations, reduce unmet demand and costs associated with overnight repositioning, and improve overall system efficiency and profitability. To approach the proposed rebalancing problem, a particle swarm optimization (PSO) is integrated with a simulation framework. PSO optimizes the rebalancing problem by iteratively refining candidate solutions, minimizing the need for extensive simulation runs, and improving computational performance. The proposed framework improves the runtime of the optimization by 27%. Through comprehensive simulations, sensitivity analyses, and PSO runs, optimal discount rates and battery level thresholds are identified that effectively nudge users to drop off scooters at the system’s preferred locations. The results highlight a significant increase in system profits, emphasizing the effectiveness of the proposed rebalancing strategy. The numerical results highlight the potential of adaptive, incentive-driven approaches to enhance electric scooter-sharing networks’ operational efficiency, computational performance, and sustainability.