Utilizing Decision Tree Method and ANFIS to Explore Real-Time Crash Risk for Urban Freeways

Traffic safety has become a severe problem on freeways in China, thus, it is important to establish a real-time crash risk model to identify traffic conditions causing crashes. In this study, the authors explore the real-time crash risk for urban freeways in China and obtain dynamic crash risk level. Crash and their matching traffic sensor data from a Beijing section of Jingha expressway in eight 5-min intervals between 0 to 40 min prior to crash occurrence was extracted during eight different periods. The crash risk value under different data conditions was defined. Then, a real-time crash risk assessment model using decision tree method and adaptive neural network fuzzy inference system (ANFIS) was proposed. Comparing several real-time crash risk assessment methods, such as logistic regression, decision tree and supported vector machine (SVM), it was found the proposed method had higher precision than others. This study can be applied to monitor real-time traffic risk on urban freeways, to assist traffic control decisions and reduce traffic accidents.

• Record URL:
  • https://doi.org/10.1061/9780784483053.211
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784483053
• Supplemental Notes:
  • © 2020 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Liu, Miaomiao
  • Wu, Zhuangzhi
  • Chen, Yongsheng
  • Zhang, Xiaodan
• Conference:
  • 20th COTA International Conference of Transportation Professionals
  • Location: Xi’an , China
  • Date: 2020-8-14 to 2020-8-16
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 2495-2508
• Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility

Subject/Index Terms

• TRT Terms: Crash causes; Crash risk forecasting; Fuzzy algorithms; Real time information; Risk assessment; Traffic safety; Urban highways
• Geographic Terms: Beijing (China)
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01768165
• Record Type: Publication
• ISBN: 9780784483053
• Files: TRIS, ASCE
• Created Date: Mar 25 2021 9:35AM