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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.14193 |
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| _version_ | 1866912383935447040 |
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| author | Abuaisha, Abdallah Shen, Bojie Harabor, Daniel Stuckey, Peter Wallace, Mark |
| author_facet | Abuaisha, Abdallah Shen, Bojie Harabor, Daniel Stuckey, Peter Wallace, Mark |
| contents | Delays in public transport are common, often impacting users through prolonged travel times and missed transfers. Existing solutions for handling delays remain limited; backup plans based on historical data miss opportunities for earlier arrivals, while snapshot planning accounts for current delays but not future ones. With the growing availability of live delay data, users can adjust their journeys in real-time. However, the literature lacks a framework that fully exploits this advantage for system-scale dynamic replanning. To address this, we formalise the dynamic replanning problem in public transport routing and propose two solutions: a "pull" approach, where users manually request replanning, and a novel "push" approach, where the server proactively monitors and adjusts journeys. Our experiments show that the push approach outperforms the pull approach, achieving significant speedups. The results also reveal substantial arrival time savings enabled by dynamic replanning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Replanning for Improved Public Transport Routing Abuaisha, Abdallah Shen, Bojie Harabor, Daniel Stuckey, Peter Wallace, Mark Artificial Intelligence Delays in public transport are common, often impacting users through prolonged travel times and missed transfers. Existing solutions for handling delays remain limited; backup plans based on historical data miss opportunities for earlier arrivals, while snapshot planning accounts for current delays but not future ones. With the growing availability of live delay data, users can adjust their journeys in real-time. However, the literature lacks a framework that fully exploits this advantage for system-scale dynamic replanning. To address this, we formalise the dynamic replanning problem in public transport routing and propose two solutions: a "pull" approach, where users manually request replanning, and a novel "push" approach, where the server proactively monitors and adjusts journeys. Our experiments show that the push approach outperforms the pull approach, achieving significant speedups. The results also reveal substantial arrival time savings enabled by dynamic replanning. |
| title | Dynamic Replanning for Improved Public Transport Routing |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.14193 |