Deploying Robust Decision Support Systems for Transit Headway Control: Rider Impacts, Human Factors and Recommendations for Scalability
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| Format: | Preprint |
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2025
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| _version_ | 1866914031171796992 |
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| author | Rodriguez, Joseph Koutsopoulos, Haris N. Zhao, Jinhua |
| author_facet | Rodriguez, Joseph Koutsopoulos, Haris N. Zhao, Jinhua |
| contents | Service reliability is critical to transit service delivery. This paper describes headway control pilots conducted in two high-ridership Chicago bus routes between 2022 and 2023. A decision support system was developed for a bus holding strategy based on a reinforcement learning approach. For the pilots, a user interface enabled supervisors to monitor service and record applied actions. The first pilot tested terminal-based holding on a route affected by missed trips from absenteeism. The analysis found improvements in reliability, and the application of control was shown to outperform days with more service. The second pilot applied en-route holding in a high-ridership bus route in Chicago. The evaluation showed wait time improvements with rippled benefits to stops downstream, and a reduction in transfer times from connecting bus and rail lines. Compliance analysis based on the supervisor logs on the app revealed mixed compliance levels from drivers, which were related to the mentality of schedule adherence and seniority. Recommendations are provided for practitioners to scale similar efforts. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_08231 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deploying Robust Decision Support Systems for Transit Headway Control: Rider Impacts, Human Factors and Recommendations for Scalability Rodriguez, Joseph Koutsopoulos, Haris N. Zhao, Jinhua Applications Computers and Society Service reliability is critical to transit service delivery. This paper describes headway control pilots conducted in two high-ridership Chicago bus routes between 2022 and 2023. A decision support system was developed for a bus holding strategy based on a reinforcement learning approach. For the pilots, a user interface enabled supervisors to monitor service and record applied actions. The first pilot tested terminal-based holding on a route affected by missed trips from absenteeism. The analysis found improvements in reliability, and the application of control was shown to outperform days with more service. The second pilot applied en-route holding in a high-ridership bus route in Chicago. The evaluation showed wait time improvements with rippled benefits to stops downstream, and a reduction in transfer times from connecting bus and rail lines. Compliance analysis based on the supervisor logs on the app revealed mixed compliance levels from drivers, which were related to the mentality of schedule adherence and seniority. Recommendations are provided for practitioners to scale similar efforts. |
| title | Deploying Robust Decision Support Systems for Transit Headway Control: Rider Impacts, Human Factors and Recommendations for Scalability |
| topic | Applications Computers and Society |
| url | https://arxiv.org/abs/2509.08231 |