Deep Reinforcement Learning-Based Holding Control for Bus Bunching under Stochastic Travel Time and Demand

Dong Liu, Feng Xiao (), Jian Luo and Fan Yang
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Dong Liu: School of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
Feng Xiao: School of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
Jian Luo: Smart City Data Center, Chengdu 610041, China
Fan Yang: Chengdu Transportation Operation Coordination Center, Chengdu 610041, China

Sustainability, 2023, vol. 15, issue 14, 1-18

Abstract: Due to the inherent uncertainties of the bus system, bus bunching remains a challenging problem that degrades bus service reliability and causes passenger dissatisfaction. This paper introduces a novel deep reinforcement learning framework specifically designed to address the bus bunching problem by implementing dynamic holding control in a multi-agent system. We formulate the bus holding problem as a decentralized, partially observable Markov decision process and develop an event-driven simulator to emulate real-world bus operations. An approach based on deep Q-learning with parameter sharing is proposed to train the agents. We conducted extensive experiments to evaluate the proposed framework against multiple baseline strategies. The proposed approach has proven to be adaptable to the uncertainties in bus operations. The results highlight the significant advantages of the deep reinforcement learning framework across various performance metrics, including reduced passenger waiting time, more balanced bus load distribution, decreased occupancy variability, and shorter travel time. The findings demonstrate the potential of the proposed method for practical application in real-world bus systems, offering promising solutions to mitigate bus bunching and enhance overall service quality.

Keywords: deep reinforcement learning; holding control; bus bunching; public transport; event-driven (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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