Parameter and density estimation from real-world traffic data: A kinetic compartmental approach
Journal article, 2022

The main motivation of this work is to assess the validity of a LWR traffic flow model to model measurements obtained from trajectory data, and propose extensions of this model to improve it. A formulation for a discrete dynamical system is proposed aiming at reproducing the evolution in time of the density of vehicles along a road, as observed in the measurements. This system is formulated as a chemical reaction network where road cells are interpreted as compartments, the transfer of vehicles from one cell to the other is seen as a chemical reaction between adjacent compartment and the density of vehicles is seen as a concentration of reactant. Several degrees of flexibility on the parameters of this system, which basically consist of the reaction rates between the compartments, can be considered: a constant value or a function depending on time and/or space. Density measurements coming from trajectory data are then interpreted as observations of the states of this system at consecutive times. Optimal reaction rates for the system are then obtained by minimizing the discrepancy between the output of the system and the state measurements. This approach was tested both on simulated and real data, proved successful in recreating the complexity of traffic flows despite the assumptions on the flux-density relation.

CFL condition

gradient descent

macroscopic model

real traffic data

Traffic reaction model

highD

Lax–Friedrichs scheme

hyperbolic PDE

finite volume scheme

viscosity solutions

parameter estimation

Author

Mike Pereira

University of Gothenburg

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Chalmers, Mathematical Sciences

Pinar Boyraz Baykas

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety, Crash Analysis and Prevention

Balázs Adam Kulcsár

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Annika Lang

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Transportation Research Part B: Methodological

0191-2615 (ISSN)

Vol. 155 210-239

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Areas of Advance

Transport

Subject Categories

Computer and Information Science

Transport Systems and Logistics

Other Mathematics

Control Engineering

DOI

10.1016/j.trb.2021.11.006

More information

Latest update

1/17/2022