eng Science and Education Publishing American Journal of Industrial Engineering 2377-4339 2020-01-25 7 1 1 9 10.12691/ajie-7-1-1 AJIE2020711 article Xinyuan Cui 1143450566@qq.com 1 Duanshu Peng 1 Business School, Sichuan University, P.R.China, 610065 With the aggravation of traffic congestion, it is more and more important to find effective solutions to traffic congestion. In order to promote the solution of traffic congestion, a data-based study on road congestion patterns in Chengdu urban area has been carried out. Firstly, the research progress at home and abroad is analyzed, and the research method based on velocity data is determined. Then the velocity data is analyzed by K-Means clustering algorithm. After verification, a 5-level traffic congestion classification table is obtained. Five different road traffic congestion patterns are identified, including very slow, slow, normal, smooth and very smooth. Then, based on the classification standard table, the road traffic conditions in Qingyang District of Chengdu City were judged, and the results were visualized by ArcMAP. According to the judgment results, the main findings are: (1) There are more congested sections and fewer smooth sections at morning peaks and late peaks than that in the afternoon and evening; (2) There is an obvious trend of better road traffic from the first ring to the third ring (from inside to outside) at morning peaks and late peaks. At the same time, 8 road sections (areas) that are prone to traffic congestion were identified. After analyzing theirs characteristics, it was found that: (1) It is prone to congestion in sections near traffic hotspots such as hospitals and scenic spots; (2) In terms of quantity, second-level roads are more congested. Secondary roads near the park and the residential areas are more likely to be congested. http://pubs.sciepub.com/ajie/7/1/1/ajie-7-1-1.pdf road congestion pattern speed visualization K-Means