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.

摘要

与正常事故相比，次生事故更容易造成重伤和死亡。然而，在揭示影响二次事故严重性的因素方面所做的努力有限。收集了五年内发生在加利福尼亚州 5 号州际公路上的事故。获得了详细的动态交通流量条件、几何特征和天气条件。首先，采用了基于随机森林 (RF) 的特征选择方法。然后，开发了支持向量机 (SVM) 模型来研究影响因素的影响。为了比较，RF 和有序逻辑 (OL) 模型也是基于相同的数据集构建的。发现 SVM 模型在解决数据可用性有限的分类问题方面具有很高的能力。更远，敏感性分析评估了解释变量对伤害严重程度的影响。解释变量，包括占用率、持续时间、车道变化频率和车道数量，被发现有助于二次事故的伤害严重程度。上游和下游的占用差异和持续时间是二次事故中高伤害的最重要因素。在事故发生后平滑这些交通状况并在事故处理和清除中快速响应，都有可能减少二次事故造成的道路创伤。上游和下游的占用差异和持续时间是二次事故中高伤害的最重要因素。在事故发生后平滑这些交通状况并在事故处理和清除中快速响应，都有可能减少二次事故造成的道路创伤。上游和下游的占用差异和持续时间是二次事故中高伤害的最重要因素。在事故发生后平滑这些交通状况并在事故处理和清除中快速响应，都有可能减少二次事故造成的道路创伤。