TY  - JOUR
PY  - 2022//
TI  - A hybrid method for traffic state classification using K-medoids clustering and self-tuning spectral clustering
JO  - Sustainability (Basel)
A1  - Shang, Qiang
A1  - Yu, Yang
A1  - Xie, Tian
SP  - e11068
EP  - e11068
VL  - 14
IS  - 17
N2  - 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>
LA  - en
SN  - 2071-1050
UR  - http://dx.doi.org/10.3390/su141711068
ID  - ref1
ER  -