Abstract
Limited efforts have been made to unveil the factors affecting the severity of secondary incidents. Compared to primary incidents, secondary incidents are more injury and fatality prone. Secondary incidents that occurred on the Interstate-5 in California within five years were collected. Detailed real-time traffic flow data, geometric characteristics and weather conditions were obtained. First, a random forest-based (RF) feature selection approach was adopted. Then, support vector machine (SVM) models were developed to investigate the effects of contributing factors. For comparison, RF and ordered logistic (OL) models were also built based on the same dataset. It was found that the SVM model has high capacity for solving classification problems with limited data availability. Further, sensitivity analysis assessed the impacts of explanatory variables on the injury severity level. The results can provide guidance for the development of countermeasures and improvement of road safety policies to potentially reduce road trauma caused by secondary incidents.
This is a short initial version, for more detailed see Li et al. [1]
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Change history
08 July 2021
In the original version of the book, in Chapter 14, the following belated corrections in references were incorporated.
(1) added to acknowledge:
“This is a short initial version, for more detailed see Li et al. (2020)”
(2) added to reference: “Li, J., Guo, J., Wijnands, J., Yu, R., Xu, C., Stevenson, M.: Assessing injury severity of secondary incidents using support vector machines. Journal of Transportation Safety & Security, 1-20 (2020)”
The chapter and book have been updated with the changes.
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Li, J., Guo, J. (2020). Injury Severity Analysis of Secondary Incidents. In: Qu, X., Zhen, L., Howlett, R.J., Jain, L.C. (eds) Smart Transportation Systems 2020. Smart Innovation, Systems and Technologies, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-15-5270-0_14
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