Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Statistics Program
Date
2020-01-25Online Publication Date
2020-01-25Print Publication Date
2020-05Embargo End Date
2022-01-25Submitted Date
2019-08-18Permanent link to this record
http://hdl.handle.net/10754/661586
Metadata
Show full item recordAbstract
A systematic approach for monitoring road traffic congestion is developed to improve safety and traffic management. To achieve this purpose, an improved observer merging the benefits of a piecewise switched linear traffic (PWSL) modeling approach and Kalman filter (KF) is introduced. The PWSL-KF observer is utilized as a virtual sensor to emulate the traffic evolution in free-flow mode. In the proposed approach, residuals from the PWSL-KF model are used as the input to k-nearest neighbors (kNN) schemes for congestion detection. Here, kNN-based Shewhart and exponential smoothing schemes are designed for discovering the traffic congestions. The proposed detectors merge the desirable properties of kNN to appropriately separating normal from abnormal features and the capability of the monitoring schemes to better identify traffic congestions. In addition, kernel density estimation has been utilized to set nonparametric control limits of the proposed detectors and compared them with their parametric counterparts. Tests on traffic measurements from the four-lane State Route 60 in California freeways show the effectiveness of the PWSL-KF-based kNN methods in supervising traffic congestions.Citation
Harrou, F., Zeroual, A., & Sun, Y. (2020). Traffic congestion monitoring using an improved kNN strategy. Measurement, 156, 107534. doi:10.1016/j.measurement.2020.107534Publisher
Elsevier BVJournal
MeasurementAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S0263224120300713ae974a485f413a2113503eed53cd6c53
10.1016/j.measurement.2020.107534