Accurate prediction and reliable significant factor analysis of incident clearance time are two main objects of traffic incident management (TIM) system, as it could help to relieve traffic congestion caused by traffic incidents. This study applies the extreme gradient boosting machine algorithm (XGBoost) to predict incident clearance time on freeway and analyze the significant factors of clearance time. The XGBoost integrates the superiority of statistical and machine learning methods, which can flexibly deal with the nonlinear data in high-dimensional space and quantify the relative importance of the explanatory variables. The data collected from the Washington Incident Tracking System in 2011 are used in this research. To investigate the potential philosophy hidden in data, K-means is chosen to cluster the data into two clusters. The XGBoost is built for each cluster. Bayesian optimization is used to optimize the parameters of XGBoost, and the MAPE is considered as the predictive indicator to evaluate the prediction performance. A comparative study confirms that the XGBoost outperforms other models. In addition, response time, AADT (annual average daily traffic), incident type, and lane closure type are identified as the significant explanatory variables for clearance time.

中文翻译：

---

基于极端梯度提升模型的交通事故清除时间预测及影响因素分析

事故清除时间的准确预测和可靠的重要因素分析是交通事故管理（TIM）系统的两个主要目标，因为它有助于缓解交通事故引起的交通拥堵。本研究应用极限梯度提升机算法（XGBoost）预测高速公路上的事故清除时间，并分析清除时间的重要因素。XGBoost整合了统计和机器学习方法的优势，可以灵活地处理高维空间中的非线性数据并量化解释变量的相对重要性。本研究使用了2011年从华盛顿事件跟踪系统收集的数据。为了调查隐藏在数据中的潜在哲学，K选择-means将数据聚类为两个聚类。XGBoost是为每个群集构建的。贝叶斯优化用于优化XGBoost的参数，MAPE被视为评估预测性能的预测指标。一项比较研究证实，XGBoost优于其他模型。此外，响应时间，AADT（年平均每日交通量），事件类型和关闭车道类型被标识为通关时间的重要解释变量。