One-Shot Traffic Assignment with Forward-Looking Penalization
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914646496116736 |
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| author | Cornacchia, Giuliano Nanni, Mirco Pappalardo, Luca |
| author_facet | Cornacchia, Giuliano Nanni, Mirco Pappalardo, Luca |
| contents | Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient route assignments. This paper introduces METIS, a cooperative, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of METIS against state-of-the-art one-shot methods. Compared to the best baseline, METIS significantly reduces CO2 emissions by 18% in Milan, 28\% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes METIS as a promising solution for optimizing TA and urban transportation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_13704 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | One-Shot Traffic Assignment with Forward-Looking Penalization Cornacchia, Giuliano Nanni, Mirco Pappalardo, Luca Multiagent Systems Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient route assignments. This paper introduces METIS, a cooperative, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of METIS against state-of-the-art one-shot methods. Compared to the best baseline, METIS significantly reduces CO2 emissions by 18% in Milan, 28\% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes METIS as a promising solution for optimizing TA and urban transportation systems. |
| title | One-Shot Traffic Assignment with Forward-Looking Penalization |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2306.13704 |