Incident indicators for freeway traffic flow models

Incident indicators for freeway traffic flow models

Dabiri, A. (TU Delft Team Azita Dabiri)
Kulcsár, Balázs (Chalmers University of Technology)

2022

Developed in this paper is a traffic flow model parametrised to describe abnormal traffic behaviour. In large traffic networks, the immediate detection and categorisation of traffic incidents/accidents is of capital importance to avoid breakdowns, further accidents. First, this claims for traffic flow models capable to capture abnormal traffic condition like accidents. Second, by means of proper real-time estimation technique, observing accident related parameters, one may even categorize the severity of accidents. Hence, in this paper, we suggest to modify the nominal Aw-Rascle (AR) traffic model by a proper incident related parametrisation. The proposed Incident Traffic Flow (ITF) model is defined by introducing the incident parameters modifying the anticipation and the dynamic speed relaxation terms in the speed equation of the AR model. These modifications are proven to have physical meaning. Furthermore, the characteristic properties of the ITF model is discussed in the paper. A multi stage numerical scheme is suggested to discretise in space and time the resulting non-homogeneous system of PDEs. The resulting systems of ODE is then combined with receding horizon estimation methods to reconstruct the incident parameters. Finally, the viability of the suggested incident parametrisation is validated in a simulation environment.

Aw-Rascle model
Macroscopic traffic flow models
Moving horizon
Multi stage numerical scheme
ODE
PDE
Traffic accidents

http://resolver.tudelft.nl/uuid:1b50f15e-9965-4596-8aa4-2753d61c4d26

https://doi.org/10.1016/j.commtr.2022.100060

2772-4247

Communications in Transportation Research, 2

Institutional Repository

journal article

© 2022 A. Dabiri, Balázs Kulcsár