Abstract
Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers.
Highlights
Autonomous vehicles (AVs) are bringing about a radical transformation in the public transportation sector. e fleet control problem associated with AVs has led to new challenges and topics of research
Two travel patterns with passenger request rates of 90 and 180 passenger requests per hour were tested. e performance was measured based on the average passenger wait time gained by the platform over 300 episodes. e results indicate that both the MADDPG and deep Q-learning (DQN) algorithms can learn the correct behavior in a single-agent environment
As expected, increasing the fleet size resulted in a decrease in passenger wait times. e results indicated that the MADDPG method was significantly better than the scheduled bus system in using a smaller fleet size to serve a greater travel demand
Summary
Autonomous vehicles (AVs) are bringing about a radical transformation in the public transportation sector. e fleet control problem associated with AVs has led to new challenges and topics of research. Autonomous vehicles (AVs) are bringing about a radical transformation in the public transportation sector. Fagnant and Kockelman [1] defined the current opportunities, barriers, and policy recommendations for AVs. Winter et al [2] determined the optimal fleet size for a shuttle service of AVs considering the minimal total cost as an objective. Boesch et al [3] explored the relationship between the served demand and the required AV fleet size. Zhang et al [5] analyzed the generalized cost for autonomous buses, which is one of the key elements for analyzing passenger preferences in using autonomous buses. Chen and Kockelman [6] explored the impact of pricing strategies on the market share of shared autonomous electric vehicles
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