Mitigating Bus Bunching with Reinforcement Learning Enhanced by Semantic Stop Embedding

• URL: http://arxiv.org/abs/2608.10207v1
• Date: Mon, 10 Aug 2026 20:21:52 GMT
• Title: Mitigating Bus Bunching with Reinforcement Learning Enhanced by Semantic Stop Embedding
• Abstract summary: Bus bunching degrades service regularity and increases passenger waiting in high-frequency transit.<n>This study introduces an LLM-assisted semantic stop representation for event-driven bus holding control.
• Score: 6.0671705325267835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bus bunching degrades service regularity and increases passenger waiting in high-frequency transit. Existing reinforcement-learning-based holding controllers primarily rely on instantaneous operational variables or route-specific stop identifiers, which provide limited information about the functional and operational context of individual stops and constrain policy reuse across routes. This study introduces an LLM-assisted semantic stop representation for event-driven bus holding control. An LLM is used offline to transform heterogeneous stop information, including physical attributes, surrounding activity context, and historical operational characteristics, into fixed semantic embeddings that are incorporated into a deep Q-learning controller without requiring real-time LLM inference. Experiments are conducted in stochastic simulations calibrated with observed data from two bus routes. Compared with the best calibrated Daganzo baseline, the semantic controller reduces headway variability, bunching events, and passenger waiting time by 32.0%, 69.2%, and 24.0%, respectively. A route-specific stop identifier does not improve the spacing-only controller, whereas semantic stop information improves headway regularity, waiting time, and holding effort, providing a more favorable overall trade-off across control objectives. Cross-route experiments further show that zero-shot transfer provides limited immediate generalization, while warm-start fine-tuning accelerates early-stage learning and improves transferred policies; cold-start training nevertheless achieves the best final performance. These findings suggest that semantic state representations can complement conventional operational states and support adaptation-based policy reuse across related transit routes.

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