Development of a Safety Prediction Method for Arterial Roads Based on Big-Data Technology and Stacked AutoEncoder-Gated Recurrent Unit

Author:

Hao Wei1ORCID,Rong Donglei2,Zhang Zhaolei2,Wu Qiyu2,Byon Young-Ji3ORCID,Yi Kefu4ORCID,Tang Jinjun5ORCID,Lyu Nengchao6ORCID

Affiliation:

1. Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha, China

2. School of Transportation Engineering, Changsha University of Science and Technology, Changsha, China

3. Department of Civil Infrastructure and Environmental Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

4. School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, China

5. School of Traffic and Transportation Engineering, Central South University, Changsha, China

6. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China

Funder

National Key Research and Development Program of China

Natural Science Foundation of China

Major Research Plan of the Natural Science Foundation of Hunan Province, China

Changsha University of Science and Technology (CSUST) Project

Science and Technology Innovation Program of Hunan Province, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Reference30 articles.

1. Impact of traffic states on freeway crash involvement rates

2. Discriminant analysis based method to develop real-time crash indicator for evaluating freeway safety;xu;J Southeast Univ,2012

3. Real time crash risk prediction model on freeways under nasty weather conditions;xu;J Jilin Univ Eng Technol Ed,2013

4. Predicting real-time traffic conflicts using deep learning

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