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Data-inspired modeling of accidents in traffic flow networks
Simone Göttlich1; Thomas Schillinger1
1 University of Mannheim, Department of Mathematics, 68131 Mannheim, Germany
The SMAI Journal of computational mathematics, Volume 11 (2025), pp. 261-287.

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.

Published online:
DOI: 10.5802/smai-jcm.125
Classification: 35R60, 90B20, 65M06
Keywords: traffic flow network model, random accidents, Hawkes process, numerical simulations

Simone Göttlich 1; Thomas Schillinger 1

1 University of Mannheim, Department of Mathematics, 68131 Mannheim, Germany 
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Simone Göttlich; Thomas Schillinger. Data-inspired modeling of accidents in traffic flow networks. The SMAI Journal of computational mathematics, Volume 11 (2025), pp. 261-287. doi : 10.5802/smai-jcm.125. https://smai-jcm.centre-mersenne.org/articles/10.5802/smai-jcm.125/

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