Accurate clearance time prediction for road incident would be helpful to evaluate the incident impacting range and provide route guiding strategy according to the predicted results, and thus reduce the travel delays caused by incidents. Currently, a number of approaches have been developed for predicting incident clearance time and investigating the effects of influential factors. Statistical and machine learning methods are the two major methodological approaches. This study aims to make a methodology review for these methods by comprehensively examining their performance in incident clearance time prediction, especially, when omitted variables present significant impacts on selected variables. Specifically, we consider four widely used statistical models: Accelerated Failure Time (AFT) model, Quantile Regression (QR) model, Finite Mixture (FM) model, and Random Parameters Hazard-Based Duration (RPHD) model, and four machine learning models: K-Nearest Neighbor (KNN) model, Support Vector Machine (SVM) model, Back Propagation Neural Network (BPNN) model, and Random Forest (RF) model as candidates. Moreover, the abilities of these methods in uncovering the underlying causality (explaining the causal effects of significant influential factors on clearance time) are also investigated. Incident clearance time data was collected on freeway road sections in Seattle, Washington State from 2009 to 2011. The conclusions can be summarized as follows: 1) the RF model and RPHD model outperform the other three models in data fitting and model prediction in their respective methodological categories; 2) three “heterogeneity” methods including RPHD, FM and QR outperform machine learning methods in model prediction as measured by MAPE; 3) machine learning methods perform stably in model prediction relative to the statistical methods; 4) incident type and lane closure type present significant effects on incident clearance time in all eight selected models.

准确的道路事故通关时间预测将有助于评估事故影响范围，并根据预测结果提供路径引导策略，从而减少事故造成的出行延误。当前，已经开发出许多方法来预测事故清除时间并调查影响因素的影响。统计和机器学习方法是两种主要的方法论方法。这项研究旨在通过全面检查它们在事故清除时间预测中的性能来对这些方法进行方法学审查，尤其是当遗漏变量对选定变量产生重大影响时。具体来说，我们考虑了四种广泛使用的统计模型：加速故障时间（AFT）模型，分位数回归（QR）模型，有限混合（FM）模型，基于随机参数的基于危害的持续时间（RPHD）模型以及四个机器学习模型：K最近邻居（KNN）模型，支持向量机（SVM）模型，反向传播神经网络（BPNN）模型，并以随机森林（RF）模型作为候选对象。此外，还研究了这些方法发现潜在因果关系的能力（解释重要影响因素对清除时间的因果关系）。从2009年至2011年在华盛顿州西雅图市的高速公路路段上收集了事故清除时间数据。结论可以归纳如下：1）RF模型和RPHD模型在各自的数据拟合和模型预测方面均优于其他三个模型方法类别；2）三种“异质性”方法，包括RPHD，在MAPE测量的模型预测中，FM和QR优于机器学习方法；3）机器学习方法相对于统计方法在模型预测中表现稳定；4）在所有八个选定模型中，事故类型和车道封闭类型对事故清除时间均具有显着影响。