Li Ruimin, Shang Pan
Institute of Transportation Engineering, Department of Civil Engineering, Tsinghua University, Heshanheng Building, Tsinghua, Beijing 100084, China.
Comput Intell Neurosci. 2014;2014:723427. doi: 10.1155/2014/723427. Epub 2014 Nov 4.
Assessing and prioritizing the duration time and effects of traffic incidents on major roads present significant challenges for road network managers. This study examines the effect of numerous factors associated with various types of incidents on their duration and proposes an incident duration prediction model. Several parametric accelerated failure time hazard-based models were examined, including Weibull, log-logistic, log-normal, and generalized gamma, as well as all models with gamma heterogeneity and flexible parametric hazard-based models with freedom ranging from one to ten, by analyzing a traffic incident dataset obtained from the Incident Reporting and Dispatching System in Beijing in 2008. Results show that different factors significantly affect different incident time phases, whose best distributions were diverse. Given the best hazard-based models of each incident time phase, the prediction result can be reasonable for most incidents. The results of this study can aid traffic incident management agencies not only in implementing strategies that would reduce incident duration, and thus reduce congestion, secondary incidents, and the associated human and economic losses, but also in effectively predicting incident duration time.
评估交通事件在主要道路上的持续时间及其影响并确定其优先级，对道路网络管理者来说是巨大的挑战。本研究考察了与各类事件相关的众多因素对其持续时间的影响，并提出了一个事件持续时间预测模型。通过分析从2008年北京事件报告与调度系统获得的交通事件数据集，研究了几种基于参数加速失效时间风险的模型，包括威布尔模型、对数逻辑模型、对数正态模型和广义伽马模型，以及所有具有伽马异质性的模型和自由度从一到十的灵活参数风险模型。结果表明，不同因素对不同的事件时间阶段有显著影响，其最佳分布各不相同。根据每个事件时间阶段的最佳风险模型，对大多数事件的预测结果可能是合理的。本研究结果不仅有助于交通事件管理机构实施能够缩短事件持续时间从而减少拥堵、二次事件以及相关的人员和经济损失的策略，还能有效预测事件持续时间。
