Method for Identifying the Traffic Congestion Situation of the Main Road in Cold-Climate Cities Based on the Clustering Analysis Algorithm

Abstract

Congestion has become a common urban disease in countries worldwide, with the acceleration of urbanization. The connotation of the congestion situation is expanded to describe, in detail, the traffic operation status and change characteristics of the main road in cold-climate cities and to provide more comprehensive identification methods and theoretical basis for cold-climate cities. It includes two aspects: the state and trend. A method to distinguish the traffic congestion state level and trend type of the main road in cold-climate cities is proposed on the basis of density clustering, hierarchical clustering, and fuzzy C-means clustering, and the temporal and spatial congestion characteristics of the main roads of cold-climate cities are explored. Research results show that we can divide the traffic congestion state into three levels: unblocked, slow, and congested. We can also divide the congestion trend into three types: aggravation, relief, and stability. This method is suitable for the identification of the main road’s congestion situation in cold-climate cities and can satisfy the spatiotemporal self-correlation and difference test. The temporal and spatial distribution rules of congestion are different under different road conditions, the volatility of the congestion degree and change speed on snowy and icy pavements, and the instability of congestion spatial aggregation are more serious than that on non-snowy and non-icy pavements. The research results are more comprehensive and objective than the existing methods.