An Improved Macroscopic Modeling for Highway Traffic Density Estimation
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2018-11-30
Print Publication Date2018-09
Permanent link to this recordhttp://hdl.handle.net/10754/631263
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AbstractEfficient and accurate estimation of traffic density plays an important role in the development of intelligent transportation systems by providing relevant information for rapid decision-making. The purpose of this study is to design a model-based procedure to estimate traffic density. Here, we design an innovative observer that combines the benefits of piecewise switched linear traffic model with Luenberger observer estimator for improving road traffic density estimation. We evaluated the proposed estimator by using traffic data from the four-lane SR-60 freeway in southern California.
CitationAbdelhafid Z, Fouzi H, Sun Y (2018) An Improved Macroscopic Modeling for Highway Traffic Density Estimation. 2018 4th International Conference on Frontiers of Signal Processing (ICFSP). Available: http://dx.doi.org/10.1109/ICFSP.2018.8552077.
SponsorsThis 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.
Conference/Event name4th International Conference on Frontiers of Signal Processing, ICFSP 2018