School of Transportation, Southeast University, 2 Si pai lou, Nanjing 210096, PR China.
HUAWEI Software Technology Co., Ltd., Yuhuatai, Nanjing 518116, PR China.
Accid Anal Prev. 2024 Oct;206:107712. doi: 10.1016/j.aap.2024.107712. Epub 2024 Jul 15.
Urban arterial and collector roads, while interconnected within the urban transportation network, serve distinct purposes, leading to different driving risk profiles. Investigating these differences using advanced methods is of paramount significance. This study aims to achieve this by primarily collecting and processing relevant vehicle trajectory data alongside driver-vehicle-road-environment data. A comprehensive risk assessment matrix is constructed to assess driving risks, incorporating multiple conflict and traffic flow indicators with statistically temporal stability. The Entropy weight-TOPSIS method and the K-means algorithm are employed to determine the risk scores and levels of the target arterial and collector roads. Using risk levels as the outcome variables and multi-scale features as the explanatory variables, random parameters models with heterogeneity in means and variances are developed to identify the determinants of driving risks at different levels. Likelihood ratio tests and comparisons of out-of-sample and within-sample prediction are conducted. Results reveal significant statistical differences in the risk profiles between arterial and collector roads. The marginal effects of significant parameters are then calculated separately for arterial and collector roads, indicating that several factors have different impacts on the probability of risk levels for arterial and collector roads, such as the number of movable elements in road landscape pictures, the standard deviation of the vehicle's lateral acceleration, the average standard deviation of speed for all vehicles on the road segment, and the number of one-way lanes on the road segment. Some practical implications are provided based on the findings. Future research can be implemented by expanding the collected data to different regions and cities over longer periods.
城市干线和集散道路虽然在城市交通网络中相互连接，但它们的功能不同，导致驾驶风险特征也不同。使用先进的方法研究这些差异至关重要。本研究旨在通过主要收集和处理相关的车辆轨迹数据以及驾驶员-车辆-道路-环境数据来实现这一目标。构建了一个综合风险评估矩阵，以评估驾驶风险，该矩阵纳入了多个冲突和交通流指标，并具有统计学上的时间稳定性。采用熵权-TOPSIS 方法和 K-均值算法确定目标干线和集散道路的风险得分和水平。使用风险水平作为因变量，多尺度特征作为解释变量，开发具有均值和方差异质性的随机参数模型，以识别不同水平下驾驶风险的决定因素。进行似然比检验和样本外及样本内预测的比较。结果表明，干线和集散道路的风险特征存在显著的统计学差异。然后分别为干线和集散道路计算显著参数的边际效应，表明有几个因素对干线和集散道路的风险水平概率有不同的影响，例如道路景观图片中可移动元素的数量、车辆横向加速度的标准差、道路段上所有车辆的平均速度标准差以及道路段上的单向车道数量。根据研究结果提供了一些实际意义。未来的研究可以通过在更长的时间内扩展收集到的数据来在不同的地区和城市中进行。
