IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929548705136640 |
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| author | Grigorev, Artur Saleh, Adriana-Simona Mihaita Khaled Ou, Yuming |
| author_facet | Grigorev, Artur Saleh, Adriana-Simona Mihaita Khaled Ou, Yuming |
| contents | The proposed IncidentResponseGPT framework - a novel system that applies generative artificial intelligence (AI) to potentially enhance the efficiency and effectiveness of traffic incident response. This model allows for synthesis of region-specific incident response guidelines and generates incident response plans adapted to specific area, aiming to expedite decision-making for traffic management authorities. This approach aims to accelerate incident resolution times by suggesting various recommendations (e.g. optimal rerouting strategies, estimating resource needs) to minimize the overall impact on the urban traffic network. The system suggests specific actions, including dynamic lane closures, optimized rerouting and dispatching appropriate emergency resources. We utilize the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank generated response plans based on criteria like impact minimization and resource efficiency based on their proximity to an human-proposed solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18550 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence Grigorev, Artur Saleh, Adriana-Simona Mihaita Khaled Ou, Yuming Machine Learning Human-Computer Interaction The proposed IncidentResponseGPT framework - a novel system that applies generative artificial intelligence (AI) to potentially enhance the efficiency and effectiveness of traffic incident response. This model allows for synthesis of region-specific incident response guidelines and generates incident response plans adapted to specific area, aiming to expedite decision-making for traffic management authorities. This approach aims to accelerate incident resolution times by suggesting various recommendations (e.g. optimal rerouting strategies, estimating resource needs) to minimize the overall impact on the urban traffic network. The system suggests specific actions, including dynamic lane closures, optimized rerouting and dispatching appropriate emergency resources. We utilize the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank generated response plans based on criteria like impact minimization and resource efficiency based on their proximity to an human-proposed solution. |
| title | IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2404.18550 |