The paper presents a model system that recognizes the distinct traffic incident duration profiles based on incident types. Specifically, a copula-based joint framework has been estimated with a scaled multinomial logit model system for incident type and a grouped generalized ordered logit model system for incident duration to accommodate for the impact of observed and unobserved effects on incident type and incident duration. The model system is estimated using traffic incident data from 2012 through 2017 for the Greater Orlando region, employing a comprehensive set of exogenous variables, including incident characteristics, roadway characteristics, traffic condition, weather condition, built environment and socio-demographic characteristics. In the presence of multiple years of data, the copula-based methodology is also customized to accommodate for observed and unobserved temporal effects (including heteroscedasticity) on incident duration. Based on a rigorous comparison across different copula models, parameterized Frank-Clayton-Frank specification is found to offer the best data fit for crash, debris, and other types of incident. The value of the proposed model system is illustrated by comparing predictive performance of the proposed model relative to the traditional single duration model on a holdout sample.

本文提出了一个基于事故类型识别交通事故持续时间简档的模型系统。具体来说，已使用基于可比性的联合Logit模型估计了事件类型的比例多项式Lo​​git模型系统，并针对事件持续时间使用了分组的​​广义有序Logit模型系统，以适应观察到的和未观察到的影响对事件类型和事件持续时间的影响。该模型系统是根据2012年至2017年大奥兰多地区的交通事故数据估算的，采用了一系列综合的外生变量，包括事故特征，道路特征，交通状况，天气状况，建筑环境和社会人口统计学特征。在存在多年数据的情况下，此外，还基于基于copula的方法进行了自定义，以适应对事件持续时间的观察到的和未观察到的时间影响（包括异方差）。基于对不同copula模型的严格比较，发现参数化的Frank-Clayton-Frank规范可为碰撞，碎屑和其他类型的事故提供最佳数据。通过在保留样本上比较提出的模型相对于传统的单持续时间模型的预测性能来说明提出的模型系统的价值。