A methodological framework for Resilience as a Service (RaaS) in multimodal urban transportation networks
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914929739563008 |
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| author | Jaber, Sara Ameli, Mostafa Mahdavi, S. M. Hassan Bhouri, Neila |
| author_facet | Jaber, Sara Ameli, Mostafa Mahdavi, S. M. Hassan Bhouri, Neila |
| contents | Public transportation systems are experiencing an increase in commuter traffic. This increase underscores the need for resilience strategies to manage unexpected service disruptions, ensuring rapid and effective responses that minimize adverse effects on stakeholders and enhance the system's ability to maintain essential functions and recover quickly. This study aims to explore the management of public transport disruptions through resilience as a service (RaaS) strategies, developing an optimization model to effectively allocate resources and minimize the cost for operators and passengers. The proposed model includes multiple transportation options, such as buses, taxis, and automated vans, and evaluates them as bridging alternatives to rail-disrupted services based on factors such as their availability, capacity, speed, and proximity to the disrupted station. This ensures that the most suitable vehicles are deployed to maintain service continuity. Applied to a case study in the Ile de France region, Paris and suburbs, complemented by a microscopic simulation, the model is compared to existing solutions such as bus bridging and reserve fleets. The results highlight the model's performance in minimizing costs and enhancing stakeholder satisfaction, optimizing transport management during disruptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_17233 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A methodological framework for Resilience as a Service (RaaS) in multimodal urban transportation networks Jaber, Sara Ameli, Mostafa Mahdavi, S. M. Hassan Bhouri, Neila Artificial Intelligence Public transportation systems are experiencing an increase in commuter traffic. This increase underscores the need for resilience strategies to manage unexpected service disruptions, ensuring rapid and effective responses that minimize adverse effects on stakeholders and enhance the system's ability to maintain essential functions and recover quickly. This study aims to explore the management of public transport disruptions through resilience as a service (RaaS) strategies, developing an optimization model to effectively allocate resources and minimize the cost for operators and passengers. The proposed model includes multiple transportation options, such as buses, taxis, and automated vans, and evaluates them as bridging alternatives to rail-disrupted services based on factors such as their availability, capacity, speed, and proximity to the disrupted station. This ensures that the most suitable vehicles are deployed to maintain service continuity. Applied to a case study in the Ile de France region, Paris and suburbs, complemented by a microscopic simulation, the model is compared to existing solutions such as bus bridging and reserve fleets. The results highlight the model's performance in minimizing costs and enhancing stakeholder satisfaction, optimizing transport management during disruptions. |
| title | A methodological framework for Resilience as a Service (RaaS) in multimodal urban transportation networks |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2408.17233 |