A real-time traffic state estimation algorithm is developed and applied to a freeway. The evolution of the traffic is defined by a second-order macroscopic model which computes, for each section of the freeway, the density, and the mean speed according to several nonlinear equations. Different extensions of the Kalman method were already applied to this model, though none of them considers the natural constraints in the state variables. In this work, a new method that incorporates those natural constraints is applied to the macroscopic model obtaining better results. To validate the proposed method, a simulation over a freeway section was made using two different tools: the macroscopic simulator called METANET and the microscopic simulator called SUMO. Promising results were obtained using both approaches.
A constrained filtering algorithm for freeway traffic state estimation
Transportmetrica A: Transport Science ; 16 , 2 ; 316-336
2020-02-07
21 pages
Article (Journal)
Electronic Resource
English
Parallelized particle filtering for freeway traffic state tracking
Tema Archive | 2007
|State Estimation in Freeway Traffic Systems
Springer Verlag | 2018
|Modeling and Filtering of Freeway Traffic Flow
NTIS | 1987
|Traffic Estimation for Proactive Freeway Traffic Control
Transportation Research Record | 1999
|Traffic Estimation for Proactive Freeway Traffic Control
Online Contents | 1999
|