Multi-objective multi-agent deep reinforcement learning to reduce bus bunching for multiline services with a shared corridor

Document Type

Journal Article

Publication Date


Subject Area

place - urban, mode - bus, operations - coordination, planning - service improvement, planning - methods, technology - automatic vehicle monitoring


Bus bunching, Deep reinforcement learning, Multi-agent system, Multi-line bus control, Multi-objective


Bus bunching is a long-standing problem in transit operation and ruining the regularity of transit service. In a typical urban transit network setting of multiple lines with a shared corridor, bus bunching becomes more frequent as there is more uncertainty inside the shared corridor. While multi-agent reinforcement learning (MARL) has been a promising scheme for learning efficient control policy in a multi-agent system, few studies have explored its applicability in multi-line transit control scenarios. In this study, we focus on a basic transit network where there are two bus lines with a shared corridor. An efficient MARL framework is proposed to learn multi-line bus holding control to avoid bus bunching. Specifically, we design observation and reward functions that incorporate multi-line information. In addition, a preference weights producer is introduced to update the objective weights towards a good trajectory evaluation during daily transit operation. In this way, we handle the multi-objective issue in multi-line control. In experimental studies, we validate the superiority of the method in real-world bus lines. Results show that the state and reward augmented with multi-line information benefit MARL in multi-line bus control. Besides, by updating preference weights towards less passenger waiting time, the regularity of transit service is further improved.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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