Real-time bus holding control on a transit corridor based on multi-agent reinforcement learning

Weiya Chen · Kunlin Zhou · Chunxiao Chen

33 citations0/0 files storedIEEE International Conference on Intelligent Transportation Systems (ITSC)

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Transportation Planning and Optimization Traffic control and management Transportation and Mobility Innovations

Abstract

Bus holding is the most used control strategy to improve bus service reliability. This paper presents a coordinated holding control framework based on multi-agent reinforcement-learning (MARL-H) to mitigate bus bunching in real-time on a bus corridor. The MARL-H framework depicts a combination of agents and methods to make decision of coordinated holdings. Coordination Graphs (CGs) are introduced in automatically selecting the coordinated holding actions in case of multiple buses dwelling at stops. Sparse Cooperative Q-learning algorithms are developed to obtain the joint coordinated holding actions due to the particularity of the sparsely structured graphs. The performance of the proposed method is evaluated by simulations. The results show the advantages of the MARL-H framework for the coordinated holding control in real-time. The proposed method could be embedded in an advanced public transportation system to improve bus operation performance.

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References

2015Planning, operation, and control of bus transport systems: A literature review 2009A headway-based approach to eliminate bus bunching: Systematic analysis and comparisons 2001The Holding Problem with Real–Time Information Available 2011Dynamic bus holding strategies for schedule reliability: Optimal linear control and performance analysis 2012How much can holding and/or limiting boarding improve transit performance?2009Hybrid predictive control for real-time optimization of public transport systems’ operations based on evolutionary multi-objective optimization 2010Hybrid predictive control strategy for a public transport system with uncertain demand 2014Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms

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2022Robust Dynamic Bus Control: a Distributional Multi-Agent Reinforcement Learning Approach 2022Cooperative Bus Holding and Stop-Skipping: A Deep Reinforcement Learning Framework 2023Multi-objective multi-agent deep reinforcement learning to reduce bus bunching for multiline services with a shared corridor 2021Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning

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