Abstract

Accurate prediction of incident duration and response strategies are two imperative aspects of traffic incident management. Past research has applied various types of regression models for predicting incident durations and quantification of associated factors. However, an important methodological aspect is unobserved heterogeneity which may be present due to several unobserved/omitted factors. Incorporation of heterogeneity has significant potential to enhance predictive capabilities as well as obtain more robust insights for designing practical incident management strategies. This study uses two frequentist/quasi-Bayesian statistical techniques—simulation-assisted random parameter and quantile regression models, to address unobserved heterogeneity and to compare both methodologies with respect to the two aspects of incident management. By using 2015 Virginia incident data related to more than 45,000 incidents, the heterogeneous associations of incident durations with several factors including detection source, incident type, roadway type, temporal factors, and incident characteristics are explored. Specifically, as the name implies, quantile regression models associations between different quantiles of incident duration and explanatory factors. This facilitates designing more appropriate strategies for small, medium and large-scale incidents. Compared to quantile regression and fixed parameter models, random parameter models can potentially give more accurate predictions of incident durations. However, they do not (typically) capture different quantiles of incident durations. By characterizing and harnessing the unobserved heterogeneity, the study proposes the concept of Incident Reduction Factors that can assist traffic incident managers and practitioners in developing customized strategies for small- and large-scale incidents. The practical implications of results are discussed from the perspectives of travelers and incident managers.

摘要

事故持续时间的准确预测和响应策略是交通事故管理的两个必要方面。过去的研究已应用各种类型的回归模型来预测事件持续时间和相关因素的量化。然而，一个重要的方法学方面是未观察到的异质性，这可能是由于几个未观察到/忽略的因素而存在的。异质性的结合具有增强预测能力以及为设计实用的事件管理策略获得更强大的洞察力的巨大潜力。本研究使用两种常客/准贝叶斯统计技术——模拟辅助随机参数和分位数回归模型，来解决未观察到的异质性，并比较两种方法在事件管理的两个方面。通过使用与 45,000 多起事故相关的 2015 年弗吉尼亚事故数据，探索事故持续时间与包括检测源、事故类型、道路类型、时间因素和事故特征在内的多个因素的异质关联。具体而言，顾名思义，分位数回归模拟事件持续时间的不同分位数和解释因素之间的关联。这有助于为小型、中型和大型事件设计更合适的策略。与分位数回归和固定参数模型相比，随机参数模型可以潜在地对事件持续时间进行更准确的预测。但是，它们（通常）不会捕获事件持续时间的不同分位数。通过表征和利用未观察到的异质性，该研究提出了事故减少因素的概念，可以帮助交通事故管理人员和从业人员制定针对小型和大型事故的定制策略。从旅行者和事件管理者的角度讨论了结果的实际意义。