Study on Data-driven Traffic Congestion Patterns in Chengdu City

Xinyuan Cui^1, and Duanshu Peng¹

¹Business School, Sichuan University, P.R.China, 610065

Cite this paper:
Xinyuan Cui and Duanshu Peng. Study on Data-driven Traffic Congestion Patterns in Chengdu City. American Journal of Industrial Engineering. 2020; 7(1):1-9. doi: 10.12691/ajie-7-1-1

Abstract

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.

Keywords:
road congestion pattern speed visualization K-Means

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Le Vine, S., C.Y. Wu, and J. Polak, A nationwide study of factors associated with household car ownership in China. Iatss Research, 2018. 42(3): p. 128-137.
[2]	Rempe, F., G. Huber, and K. Bogenberger, Spatio-Temporal Congestion Patterns in Urban Traffic Networks, in International Symposium on Enhancing Highway Performance, K. Lemke, J. Geistefeldt, and J. JajkoSchlichter, Editors. 2016. p. 513-524.
[3]	Wen, H.M., J.P. Sun, and X. Zhang, Study on Traffic Congestion Patterns of Large City in China Taking Beijing as an Example, in 9th International Conference on Traffic and Transportation Studies, B. Mao, et al., Editors. 2014. p. 482-491.
[4]	Lozano, A., G. Manfredi, and L. Nieddu, An algorithm for the recognition of levels of congestion in road traffic problems. Mathematics and Computers in Simulation, 2009. 79(6): p. 1926-1934.
[5]	Jeong, Y., et al., K-means Data Clustering with Memristor Networks. Nano Letters, 2018. 18(7): p. 4447-4453.
[6]	Yang, Y.J., et al., Efficient traffic congestion estimation using multiple spatio-temporal properties. Neurocomputing, 2017. 267: p. 344-353.
[7]	Kong, X.J., et al., Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Generation Computer Systems-the International Journal of Escience, 2016. 61: p. 97-107.
[8]	Xu, L., Y. Yue, and Q.Q. Li, Identifying Urban Traffic Congestion Pattern from Historical Floating Car Data, in Intelligent and Integrated Sustainable Multimodal Transportation Systems Proceedings from the 13th Cota International Conference of Transportation Professionals, L. Zhang, et al., Editors. 2013. p. 2084-2095.
[9]	Kan, Z.H., et al., Traffic congestion analysis at the turn level using Taxis' GPS trajectory data. Computers Environment and Urban Systems, 2019. 74: p. 229-243.