School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong, 510641, PR China.
Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya 464-8603, Japan.
J Safety Res. 2021 Feb;76:248-255. doi: 10.1016/j.jsr.2020.12.014. Epub 2021 Jan 7.
It has been demonstrated that weather conditions have significant impacts on freeway safety. However, when employing an econometric model to examine freeway crash injury severity, most of the existing studies tend to categorize several different adverse weather conditions such as rainy, snowy, and windy conditions into one category, "adverse weather," which might lead to a large amount of information loss and estimation bias. Hence, to overcome this issue, real-time weather data, the value of meteorological elements when crashes occurred, are incorporated into the dataset for freeway crash injury analysis in this study.
Due to the possible existence of spatial correlations in freeway crash injury data, this study presents a new method, the spatial multinomial logit (SMNL) model, to consider the spatial effects in the framework of the multinomial logit (MNL) model. In the SMNL model, the Gaussian conditional autoregressive (CAR) prior is adopted to capture the spatial correlation. In this study, the model results of the SMNL model are compared with the model results of the traditional multinomial logit (MNL) model. In addition, Bayesian inference is adopted to estimate the parameters of these two models.
The result of the SMNL model shows the significance of the spatial terms, which demonstrates the existence of spatial correlation. In addition, the SMNL model has a better model fitting ability than the MNL model. Through the parameter estimate results, risk factors such as vertical grade, visibility, emergency medical services (EMS) response time, and vehicle type have significant effects on freeway injury severity. Practical Application: According to the results, corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. For example, steep slopes should be avoided if possible, and in-lane rumble strips should be recommended for steep down-slope segments. Besides, traffic volume proportion of large vehicles should be limited when the wind speed exceeds a certain grade.
已证实，天气条件对高速公路安全有重大影响。然而，在使用计量经济学模型研究高速公路碰撞伤害严重程度时，大多数现有研究倾向于将几种不同的恶劣天气条件（如雨天、雪天和大风天气）归为一类，即“恶劣天气”，这可能导致大量信息丢失和估计偏差。因此，为了克服这个问题，本研究将实时天气数据（即发生碰撞时气象要素的值）纳入高速公路碰撞伤害分析的数据集。
由于高速公路碰撞伤害数据可能存在空间相关性，本研究提出了一种新方法，即空间多项逻辑回归（SMNL）模型，以便在多项逻辑回归（MNL）模型的框架中考虑空间效应。在 SMNL 模型中，采用高斯条件自回归（CAR）先验来捕捉空间相关性。在本研究中，将 SMNL 模型的模型结果与传统多项逻辑回归（MNL）模型的模型结果进行比较。此外，采用贝叶斯推断来估计这两个模型的参数。
SMNL 模型的结果显示了空间项的重要性，表明存在空间相关性。此外，SMNL 模型比 MNL 模型具有更好的模型拟合能力。通过参数估计结果，垂直坡度、能见度、紧急医疗服务（EMS）响应时间和车辆类型等风险因素对高速公路伤害严重程度有显著影响。
根据研究结果，提出了针对高速公路路面设计、交通管理和车辆设计的相应对策，以提高高速公路安全性。例如，如果可能，应避免陡峭的坡度，并建议在陡峭的下坡路段设置车道内颠簸带。此外，当风速超过一定等级时，应限制大型车辆的交通量比例。
