Study on Traffic Congestion Patterns of Large City in China Taking Beijing as an Example

Urban traffic congestion has different typical characteristics under the influence of different conditions, such as different day of week, holiday and weather etc. It is necessary to set up the relationships between traffic congestion patterns and those influencing factors, when conducting macroscopic analysis on the causes of traffic congestion. Based on Traffic Performance Index (TPI), a dynamic macroscopic index showing the whole area congestion intensity developed in 2007, typical congestion patterns are identified by using clustering method. A comparative analysis is conducted on setting rules for different clustering indexes. TPI pattern curves are derived and verified under combinations of date, transportation demand management policy, holiday, weather condition and etc., according to the actual traffic operational status. The analysis and verification results show that the method used in this paper is effective and feasible. TPI patterns indicate that traffic congestion has inherent characteristics which are primary and essential for transportation managers. This paper lays the foundation for traffic congestion prediction and early warning and proactive alleviation of traffic congestions.

• Record URL:
  https://doi.org/10.1016/j.sbspro.2014.07.227
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1877042814041469
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770428
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Wen, Huimin
  • Sun, Jianping
  • Zhang, Xi
• Conference:
  • 9th International Conference on Traffic and Transportation Studies (ICTTS’2014)
  • Location: Shaoxing Zhejiang Province, China
  • Date: 2014-8-1 to 2014-8-2
• Publication Date: 2014-7-14

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 482-491
• Serial:
  • Procedia - Social and Behavioral Sciences
  • Volume: 138
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0428
  • Serial URL: http://www.sciencedirect.com/science/journal/18770428/53
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Cluster analysis; Data mining; Large cities; Traffic congestion; Traffic forecasting
• Geographic Terms: Beijing (China)
• Subject Areas: Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01535380
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 27 2014 10:47AM