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Data-inspired modeling of accidents in traffic flow networks using the Hawkes process

2023/05/05 by Simone Göttlich, Göttlich, Simone, Thomas Schillinger +1
Engineering · #Traffic Prediction and Management Techniques

paper · pdf · doi:10.48550/arxiv.2305.03469

Abstract

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.

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