An optimized real-time crash prediction model on freeway with over-sampling techniques based on support vector machine

Article type: Research Article

Authors: You, Jinming | Wang, Junhua^{; *} | Fang, Shouen | Guo, Jingqiu

Affiliations: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China

Correspondence: [*] Corresponding author. Junhua Wang, Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China. Tel.: +86 13061765132; Fax: +86 21 69585717; E-mail: [email protected].

Abstract: Real-time crash prediction is crucial in traffic management on freeway to improve traffic safety. This study presents an optimized crash prediction model on freeway with over-sampling techniques based on Support Vector Machine (SVM). The model was constructed with traffic data collected by discrete loop detectors from a 48.7 km segment on the G60 Freeway, Shanghai, China. Matched case-control method and SVM were applied to identify the high-risk traffic flow status. Two kinds of over-sampling techniques have been conducted to optimize the raw samples. The adaptive synthetic over-sampling technique presents better performance than the synthetic minority over-sampling technique according to the nonparametric test. The results indicate that SVM classifiers with the adaptive synthetic over-sampling technique improve the accuracy and robustness when dealing with imbalanced data. Mean Impact Value method was employed to rank the contributing factors leading to crash. This research contributes to more targeted strategies for real-time safety management of freeway.

Keywords: Real-time, crash prediction, freeway, over-sampling technique, support vector machine

DOI: 10.3233/JIFS-162155

Journal: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 555-562, 2017