Data-inspired modeling of accidents in traffic flow networks using the Hawkes process
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866915008265322496 |
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| author | Göttlich, Simone Schillinger, Thomas |
| author_facet | Göttlich, Simone Schillinger, Thomas |
| contents | We consider hyperbolic partial differential equations (PDEs) for a dynamic description of the traffic behavior in road networks. These equations are coupled to a Hawkes process that models traffic accidents taking into account their self-excitation property which means that accidents are more likely in areas in which another accident just occurred. We discuss how both model components interact and influence each other. A data analysis reveals the self-excitation property of accidents and determines further parameters. Numerical simulations using risk measures underline and conclude the discussion of traffic accident effects in our model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_03469 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-inspired modeling of accidents in traffic flow networks using the Hawkes process Göttlich, Simone Schillinger, Thomas Numerical Analysis 35R60, 90B20, 65M06 We consider hyperbolic partial differential equations (PDEs) for a dynamic description of the traffic behavior in road networks. These equations are coupled to a Hawkes process that models traffic accidents taking into account their self-excitation property which means that accidents are more likely in areas in which another accident just occurred. We discuss how both model components interact and influence each other. A data analysis reveals the self-excitation property of accidents and determines further parameters. Numerical simulations using risk measures underline and conclude the discussion of traffic accident effects in our model. |
| title | Data-inspired modeling of accidents in traffic flow networks using the Hawkes process |
| topic | Numerical Analysis 35R60, 90B20, 65M06 |
| url | https://arxiv.org/abs/2305.03469 |