Zhao Xiaohua, Ding Yang, Yao Ying, Zhang Yunlong, Bi Chaofan, Su Yuelong
Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, 100124, China.
Zachry Department of Civil Engineering, Texas A&M University, 77843, the USA.
J Safety Res. 2022 Feb;80:27-38. doi: 10.1016/j.jsr.2021.11.002. Epub 2021 Nov 29.
Urban expressway interchanges have become accident-prone sites owing to the accelerated increase in motor-vehicle ownership. This study explored the impact of factors, including day of the week, time of day, congestion level, traffic control devices, and road conditions, on road safety risk levels in the interchange area of an urban expressway based on aggregate driving behavior data.
A large amount of aggregate driving behavior data were obtained from AutoNavi navigation software. The database was built by matching various types of data and observing their characteristics. Day of the week, time of day, congestion level, road conditions (number of lanes, traffic disturbance, and traffic control devices [the type of advance guide sign system, number of warning signs, and the complexity of the diagrammatic guide sign]) were identified as the explanatory variables. The traffic order index (TOI), based on driving behavior and speed variation, was used to evaluate the road safety risk levels, including risky roads, general roads, and safe roads, which served as the response variables. The multinomial logit model (MNL) was developed to explore the impact of various factors, including traffic control devices and road conditions, on road safety risk levels.
The results showed that the factors that significantly influence risky roads include day of the week, number of lanes, congestion level (slow moving), traffic disturbance (with the merge or diverge within 500 m), type of advance guide sign system (three-level advance guide sign system), and complexity of diagrammatic guide signs (low or medium complexity). Practical Applications: This study could offer plausible suggestions for traffic management departments for the rehabilitation of road conditions and traffic control devices in urban expressway interchange areas.
由于机动车保有量的加速增长，城市快速路互通式立交已成为事故多发地点。本研究基于汇总的驾驶行为数据，探讨了包括星期几、一天中的时间、拥堵程度、交通控制设备和道路状况等因素对城市快速路互通式立交区域道路安全风险水平的影响。
从高德导航软件获取了大量汇总的驾驶行为数据。通过匹配各类数据并观察其特征建立了数据库。将星期几、一天中的时间、拥堵程度、道路状况（车道数量、交通干扰和交通控制设备[前置引导标志系统类型、警告标志数量和图示引导标志的复杂程度]）确定为解释变量。基于驾驶行为和速度变化的交通秩序指数（TOI）用于评估道路安全风险水平，包括危险道路、一般道路和安全道路，这些作为响应变量。开发了多项logit模型（MNL）以探讨包括交通控制设备和道路状况在内的各种因素对道路安全风险水平的影响。
结果表明，对危险道路有显著影响的因素包括星期几、车道数量、拥堵程度（行驶缓慢）、交通干扰（500米内有合流或分流）、前置引导标志系统类型（三级前置引导标志系统）以及图示引导标志的复杂程度（低或中等复杂程度）。实际应用：本研究可为交通管理部门改善城市快速路互通式立交区域的道路状况和交通控制设备提供合理建议。
