Document Type
Article
Publication Date
11-20-2018
Abstract
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.
Recommended Citation
Alidad Ahmadi, Arash Jahangiri, Vincent Berardi & Sahar Ghanipoor Machiani (2020) Crash severity analysis of rear-end crashes in California using statistical and machine learning classification methods, Journal of Transportation Safety & Security, 12:4, 522-546, https://doi.org/10.1080/19439962.2018.1505793
Copyright
Taylor & Francis
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Comments
This is an Accepted Manuscript of an article published in Journal of Transportation Safety & Security, volume 12, issue 4, in 2020, available online at https://doi.org/10.1080/19439962.2018.1505793. It may differ slightly from the final version of record.
The Creative Commons license below applies only to this version of the article.