One-way station-based electric vehicle (EV) carsharing services gain significant popularity. However, supply-demand imbalance caused by unreasonable service allocation strategies has been a serious issue, which negatively affects users’ convenience and operators’ profit. Researchers commonly utilized operator-based allocation strategies, leading to large fleet allocation costs and low service efficiency. This issue urgently needs to be resolved.
To solve the existing problems, this paper proposes a novel user-based allocation strategy based on the dynamic price incentive mechanism (DPIM), considering operator cost-benefit factors, user heterogeneous demands and service system balance. Subsequently, an innovative simulation-optimization method framework is developed to conduct DPIM. In this framework, Monte Carlo simulation is utilized to determine real-time station optimal inventory. Then, we establish a user-based service allocation optimization model. An adaptive genetic algorithm (AGA) is utilized to maximize service revenue. Finally, in real-world urban and suburb stations of Shanghai, we verify the feasibility and effectiveness of the proposed DPIM by comparing different benchmark methods and scenario scales.
Results indicate that the overall system balance is significantly promoted. Daily maximum profit has improved up to 89,023 RMB under DPIM. Sensitivity analysis has proved that both economic and luxury EV users are price sensitive. Proper implementation of DPIM can increase vehicle utilization and reduce service costs during the peak commuting periods.
This research offers significant theoretical and practical insights for more efficient, profitable and sustainable carsharing operations in urban transportation systems. The novel simulation-optimization method framework is designed to conduct EV carsharing inventory strategic decisions and allocation operational planning. The proposed DPIM advances dynamic pricing theory by integrating user-based service allocation. Then, AGA significantly improves revenue optimization compared to existing metaheuristic approaches in shared mobility resource allocation. Meanwhile, our research demonstrates how public-private partnerships of EV carsharing services can create more sustainable and efficient urban transportation ecosystems. Our findings have highlighted the carsharing potential in mega- and smart-city development.
