A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering

Qiang Shang, Yang Yu () and Tian Xie
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Qiang Shang: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Yang Yu: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Tian Xie: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China

Sustainability, 2022, vol. 14, issue 17, 1-20

Abstract: As an important part of intelligent transportation systems, traffic state classification plays a vital role for traffic managers when formulating measures to alleviate traffic congestion. The proliferation of traffic data brings new opportunities for traffic state classification. In this paper, we propose a hybrid new traffic state classification method based on unsupervised clustering. Firstly, the k-medoids clustering algorithm is used to cluster the daily traffic speed data from multiple detection points in the selected area, and then the cluster-center detection points of the cluster with congestion are selected for further analysis. Then, the self-tuning spectral clustering algorithm is used to cluster the speed, flow, and occupancy data of the target detection point to obtain the traffic state classification results. Finally, several state-of-the-art methods are introduced for comparison, and the results show that performance of the proposed method are superior to comparable methods.

Keywords: traffic state classification; traffic flow; spectral clustering; k-medoids clustering (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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