MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Sci Total Environ. 2023 Jun 15;877:162938. doi: 10.1016/j.scitotenv.2023.162938. Epub 2023 Mar 17.
Existing studies mainly focus on the relationship between real-time weather and traffic crash injury severity, while few scholars have investigated the operation risk levels caused by traffic incidents. Identifying weather-related factors that affect the incident-induced delay is helpful for estimating the delay levels when an incident occurs. Accordingly, the present study profoundly explores the relationship between weather conditions and traffic delays caused by traffic incidents.
The traffic incident and weather datasets from January 1 to December 31, 2020, in New York State are used. To that end, the hazard-based duration and multinomial logit modeling frameworks are employed to determine the effect of weather conditions on the duration of traffic delay and the delay severity, respectively. More importantly, to account for multiple layers of unobserved heterogeneity, a random parameter with heterogeneity in means approach is introduced into the above two models.
(1) The strong breeze (wind speed over 8 m/s) and low visibility (visibility under 5 km) significantly affect the duration of delay. (2) Hot day (between 20 and 30 °C) has a 344.03 % greater probability of minor delay. A strong breeze has a higher probability of severe delay. The low visibility is found to increase the estimated odds of moderate delay and severe delay by 51.15 % and 13.39 %, respectively. In comparison, the normal visibility (between 10 and 20 km) significantly decreases the estimated odds of severe delay by 119.17 %.
Compared with other weather factors, wind speed, temperature, and visibility have the greatest impact on the traffic delay levels after a traffic accident, and there are significant differences in the impact under different delay severity. Findings from this study will help policymakers to establish comprehensive differentiating security measures to resolve traffic delays.
现有研究主要关注实时天气与交通碰撞伤害严重程度之间的关系，而很少有学者研究交通事件引起的运营风险水平。确定与天气相关的因素会影响因事故引起的延误，这有助于在发生事故时估计延误水平。因此，本研究深入探讨了天气条件与交通事件引起的交通延误之间的关系。
使用了 2020 年 1 月 1 日至 12 月 31 日纽约州的交通事件和天气数据集。为此，采用基于危险的持续时间和多项逻辑回归建模框架，分别确定天气条件对交通延误持续时间和延误严重程度的影响。更重要的是，为了考虑多个层次的未观察到的异质性，引入了带有均值异质性的随机参数方法到上述两个模型中。
（1）强风（风速超过 8 m/s）和低能见度（能见度低于 5 km）显著影响延误持续时间。（2）炎热天气（20-30°C）发生轻微延误的可能性增加 344.03%。强风发生严重延误的可能性更高。发现低能见度会分别增加中度延误和严重延误的估计赔率 51.15%和 13.39%。相比之下，正常能见度（10-20 km）会显著降低严重延误的估计赔率 119.17%。
与其他天气因素相比，风速、温度和能见度对交通事故后交通延误水平的影响最大，并且在不同延误严重程度下的影响存在显著差异。本研究的结果将有助于政策制定者制定全面的差异化安全措施来解决交通延误问题。
