Details of Research Outputs

Inefficient and unreliable public transportation systems remain a significant challenge in growing cities, with bus bunching being a key contributor to passenger dissatisfaction. Despite numerous proposed holding strategies to mitigate this issue, there is a lack of a standardized testbed for their comprehensive evaluation. This paper presents an open-source, extensible simulation platform that enables the development and benchmarking of bus holding strategies in a unified environment. It accommodates both model-based and model-free reinforcement learning (RL) control strategies, providing a systematic approach to assess their performance under various operating conditions. Holding control strategies can be customized by users within our platform, provided they create a class that fulfills the basic requirements of the exposed application programming interface (API). The platform is designed to be easily extensible, allowing users to incorporate real-world datasets and customize detailed operational features. We demonstrate the platform’s capabilities by comparing three holding strategies: a model-based forward headway control method and two RL-based approaches. Experimental results highlight the importance of comprehensive evaluations, as the relative performance of different strategies varies under different holding time budgets. The proposed simulation platform aims to facilitate more robust, comparable, and reproducible research in bus operation control strategies, ultimately leading to improved bus service reliability in real-world implementations.