Charge or Pick Up? Optimizing E-Taxi Management: A Dual-Stage Heuristic Coordinated Reinforcement Learning Approach

In recent years, the rapid adoption of electric vehicles (EVs) in the taxi industry has transformed traditional taxi-hailing systems into electric taxi (E-taxi) hailing systems. As a result, it is crucial to develop effective strategies for optimizing E-taxi management by considering both passenger-taxi matching and charging planning. In this paper, we first formalize the E-taxi management optimization problem as a Markov decision problem with dynamic state and heterogeneous action. We then propose a dual-stage heuristic coordinated reinforcement learning (RL) approach that incorporates advanced feature selection and heuristic allocation strategies. Our approach consists of two main stages. In the first stage, we introduce the feature-guided state dimensionality stabilization proximal policy optimization (PPO) method to address dynamic state dimensions by a feature selection method, and enabling E-taxis to decide whether to charge or pick up passengers. In the second stage, we propose a heuristic coordinated assignment method to further allocate charging stations and passengers for the E-taxis, and provide the RL network in the first stage with rewards based on the results. This approach effectively tackles the challenge of RL processing of heterogeneous action spaces (charge and pick up). We evaluate our proposed method in a real-world E-taxi environment and find that it significantly enhances the experience for both E-taxis and passengers. Specifically, due to our method’s rational planning for passenger pick-up and charging, E-taxis can increase their revenue by 20% compared to traditional RL methods or random scheduling approaches. As for passengers, since the taxis have more efficiently planned their charging behavior, the probability of their orders being answered increases by 15%, while their waiting time is reduced by 55%. These achievements contribute to the advancement of E-taxi management strategies and promote the widespread adoption of electric vehicles, ultimately supporting the transition to a more sustainable transportation system. Note to Practitioners—The increasing adoption of electric vehicles in the taxi industry has led to the need for effective E-taxi management strategies that consider both passenger-taxi matching and charging planning. In this study, we introduce a dual-stage heuristic coordinated reinforcement learning approach that addresses these challenges by integrating a feature-guided state dimensionality stabilization proximal policy optimization method and a heuristic coordinated assignment method. Our approach offers several practical benefits for E-taxi service providers, drivers, and passengers. For E-taxi service providers, the proposed method improves E-taxi dispatch efficiency, resulting in a more effective use of available resources and potentially increasing overall revenue. For E-taxi drivers, our approach leads to better planning of charging and passenger pick-up decisions, increasing their earnings by 20% compared to traditional methods, and reducing the average occurrence of low battery status from more than 4 times every 10 hours to less than 1 time. Passengers, on the other hand, experience improved service quality due to the more efficient E-taxi management. The probability of their orders being answered increases by 15%, and their waiting time is reduced by 100%. These improvements contribute to an enhanced user experience and may encourage further adoption of E-taxis as a sustainable transportation solution. The proposed method can be integrated into existing E-taxi hailing platforms, such as DiDi and Uber, to enhance their dispatch and charging management capabilities. As the global trend towards sustainable transportation continues to grow, our approach provides valuable insights and a practical solution for the efficient management of E-taxi fleets in modern urban environments.