@article{ref1,
title="A hybrid method for traffic state classification using K-medoids clustering and self-tuning spectral clustering",
journal="Sustainability (Basel)",
year="2022",
author="Shang, Qiang and Yu, Yang and Xie, Tian",
volume="14",
number="17",
pages="e11068-e11068",
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.<p /> <p>Language: en</p>",
language="en",
issn="2071-1050",
doi="10.3390/su141711068",
url="http://dx.doi.org/10.3390/su141711068"
}