Crash severity analysis of rear-end crashes in California using statistical and machine learning classification methods

• Published in: Journal of transportation safety & security Vol. 12; no. 4; pp. 522 - 546
• Main Authors: Ahmadi, Alidad, Jahangiri, Arash, Berardi, Vincent, Machiani, Sahar Ghanipoor
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 20.04.2020; Taylor & Francis Ltd
• Subjects: crash severity classification; Crashes; Datasets; Drivers; Learning algorithms; Machine learning; mixed multinomial logit; Road design; Safety engineering; Statistical analysis; support vector machine; Support vector machines; Traffic safety; Vehicle safety; Vehicles; United States > US; California
• ISSN: 1943-9962; 1943-9970
• DOI: 10.1080/19439962.2018.1505793

Investigating drivers' injury level and detecting contributing factors that aggravate the damage level imposed on drivers and vehicles is a critical subject in the field of crash analysis. In this study, a comprehensive vehicle-by-vehicle crash data set is developed by integrating 5 years of data from California crash, vehicles involved, and road databases. The data set is used to model the severity of rear-end crashes for comparing three analytic techniques: multinomial logit, mixed multinomial logit, and support vector machine (SVM). The results of the crash severity models and the role of contributing factors to the severity outcome of rear-end crashes are extensively discussed. In terms of prediction performance, all three models yielded comparable results; although, the SVM performed slightly better than the other two methods. The results from this study will inform aspects of our driver safety education and design, either vehicle or roadway design, required to be improved to alleviate the probability of severe injuries.