Integrating Model-based Observer and Kullback-Leibler Metric for Estimating and Detecting Road Traffic Congestion

Zeroual, Abdelhafid; Harrou, Fouzi; Sun, Ying; Messai, Nadhir

Integrating Model-based Observer and Kullback-Leibler Metric for Estimating and Detecting Road Traffic Congestion

Files

08444428.pdf (2.51 MB)

Type

Article

Authors

Zeroual, Abdelhafid
Harrou, Fouzi

Sun, Ying

Messai, Nadhir

KAUST Department

Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Statistics Program

KAUST Grant Number

OSR-2015-CRG4-2582

Online Publication Date

2018-08-22

Print Publication Date

2018-10-15

Date

2018-08-22

Abstract

Efficient detection of traffic congestion plays an important role in the development of intelligent transportation systems by providing useful information for rapid decisionmaking. The aim of this study is to design an approach for road traffic congestion estimation and detection. Here, we design an innovative observer by integrating a hybrid piecewise switched linear traffic model (PWSL) with Luenberger observer estimator for enhanced road traffic density estimation. This observer termed PWSL-LO combines the flexibility of the PWSL model with the simplicity and efficiency of Luenberger observer to estimate the unmeasured traffic density. Moreover, this paper proposes an approach to monitor traffic congestion based on Kullback-Leibler distance (KLD) and exponential weighted moving average (EWMA) procedure. Residuals from the PWSLLO model are used as the input for KLD-EWMA scheme for congestion detection. This is motivated by the high capacity of KLD to quantitatively discriminate between two distributions. Here, the EWMA scheme is applied to the KLD measurements for congestion detection. Moreover, wavelet-based multiscale filter, a powerful feature/noise separation tool, is used to deal with the problem of measurement noise in the data. We evaluated the detection performance of this scheme by using traffic data from the four-lane SR-60 freeway in southern California. The proposed approach showed good abilities to estimate, monitor traffic congestions and to handle noisy traffic data.

Citation

Integrating Model-based Observer and Kullback-Leibler Metric for Estimating and Detecting Road Traffic Congestion (2018). IEEE Sensors Journal: 1–1. Available: http://dx.doi.org/10.1109/JSEN.2018.2866678.

Acknowledgements

This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.