Assessing injury severity of secondary incidents using support vector machines

Abstract

Compared to normal incidents, secondary incidents are more likely to result in severe injuries and fatalities. However, limited efforts have been made to unveil the factors affecting the severity of secondary incidents. Incidents that occurred on the Interstate-5 in California within five years were collected. Detailed dynamic traffic flow conditions, 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. Explanatory variables, including occupancy, duration, frequency of lanes changes, and number of lanes, were found to contribute to injury severity of secondary incidents. Occupancy difference between upstream and downstream and duration are the most significant factors of high injuries in secondary incidents. Smoothing these traffic conditions after an incident occurs and responding fast in incident handing and clearance both have potential to reduce road trauma caused by secondary incidents.

Keywords:

• injury severity
• secondary incident
• support vector machine
• dynamic traffic condition

Additional information

Funding

This work was supported by the National Key R&D Program of China under Grant [2018YFB1600500], National Natural Science Foundation of China [71671126,51708421].