Utilizing Decision Tree Method and ANFIS to Explore Real-Time Crash Risk for Urban Freeways
Author:
Affiliation:
1. School of Transportation Science and Engineering, Beihang Univ., Beijing, China.
2. School of Computer Science and Engineering, Beihang Univ., Beijing, China.
3. Research Institute of Highway, Ministry of Transport, Beijing, China.
Publisher
American Society of Civil Engineers
Link
http://ascelibrary.org/doi/pdf/10.1061/9780784483053.211
Reference26 articles.
1. Abdel-Aty M. and Pande A. (2004). "Classification of real-time traffic speed patterns to prediction crashes on freeways." Presented at 83th Annual Meeting of Transportation Research Board CD-ROM Washington DC.
2. Linking roadway geometrics and real-time traffic characteristics to model daytime freeway crashes using generalized estimating equations for correlated data;Abdel-Aty M.;Transportation Research Record,2004
3. Predicting freeway crashes from loop detector data using matched case-control logistic regression;Abdel-Aty M.;Transportation Research Record,2004
4. Identifying crash propensity using specific traffic speed conditions;Abdel-Aty M.;Journal of Safety Research,2005
5. Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model;Chang L. Y.;Safety Science,2012
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