Use of Support Vector Machine Models for Real-Time Prediction of Crash Risk on Urban Expressways

This study adopted a novel methodology—a support vector machine (SVM) with two penalty parameters—for the evaluation of real-time crash risk on urban expressway segments by using dual-loop detector data. The purpose of this study was to develop a model that can effectively identify traffic conditions prone to crashes and support implementation of proactive traffic safety management. On the basis of crash data and the corresponding detector data collected on expressways of Shanghai, China, different combinations of dual-loop detector data and time segments before crashes were used to develop the optimal crash risk estimation model by SVM. The transferability of the SVM model was assessed by examining whether the model developed on one expressway was applicable to other similar ones. In addition, the prediction results and transferability of the SVM model were compared with those given by other frequently used classification algorithms, including logistic regression, Bayesian networks, native Bayes classifier, k-nearest neighbor, and back propagation neural network. The results showed that the SVM model was more suitable to the prediction of real-time crash risk with small-scale data than other algorithms, with its accuracy in classifying crashes reaching a best of 80%. A comparison to similar studies by other researchers implied that the proposed model achieved better prediction accuracy.

• Record URL:
  https://doi.org/10.3141/2432-11
• Summary URL:
  http://scholar.google.com/scholar_lookup?title=Use+of+Support+Vector+Machine+Models+for+Real-Time+Prediction+of+Crash+Risk+on+Urban+Expressways&author=J.+Sun&author=J.+Sun&author=P.+Chen&publication_year=2014
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780309295208
• Authors:
  • Sun, Jian
  • Sun, Jie
  • Chen, Peng
• Publication Date: 2014

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 91–98
• Monograph Title: Safety Data, Analysis, and Evaluation 2014
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 2432
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Algorithms; Crash risk forecasting; Mathematical models; Mathematical prediction; Real time information; Traffic safety; Urban highways
• Uncontrolled Terms: Support vector machines
• Geographic Terms: Shanghai (China)
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; I80: Accident Studies;

Filing Info

• Accession Number: 01518820
• Record Type: Publication
• ISBN: 9780309295208
• Report/Paper Numbers: 14-3348
• Files: TRIS, TRB, ATRI
• Created Date: Mar 21 2014 11:27AM