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

Donghe Li, Chunlin Hu, Qingyu Yang, Pengtao Song, Feiye Zhang, Dou An

Abstract:

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 learn
ing (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 support
ing the transition to a more sustainable transportation system.