Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada; Southeast University Road #2, Nanjing, 211189, China; School of Transportation, Southeast University Si Pai Lou #2, Nanjing, 210096, China.
Southeast University Road #2, Nanjing, 211189, China; School of Transportation, Southeast University Si Pai Lou #2, Nanjing, 210096, China.
Accid Anal Prev. 2018 Jun;115:118-127. doi: 10.1016/j.aap.2018.03.006. Epub 2018 Mar 17.
Bicyclists running the red light at crossing facilities increase the potential of colliding with motor vehicles. Exploring the contributing factors could improve the prediction of running red-light probability and develop countermeasures to reduce such behaviors. However, individuals could have unobserved heterogeneities in running a red light, which make the accurate prediction more challenging. Traditional models assume that factor parameters are fixed and cannot capture the varying impacts on red-light running behaviors. In this study, we employed the full Bayesian random parameters logistic regression approach to account for the unobserved heterogeneous effects. Two types of crossing facilities were considered which were the signalized intersection crosswalks and the road segment crosswalks. Electric and conventional bikes were distinguished in the modeling. Data were collected from 16 crosswalks in urban area of Nanjing, China. Factors such as individual characteristics, road geometric design, environmental features, and traffic variables were examined. Model comparison indicates that the full Bayesian random parameters logistic regression approach is statistically superior to the standard logistic regression model. More red-light runners are predicted at signalized intersection crosswalks than at road segment crosswalks. Factors affecting red-light running behaviors are gender, age, bike type, road width, presence of raised median, separation width, signal type, green ratio, bike and vehicle volume, and average vehicle speed. Factors associated with the unobserved heterogeneity are gender, bike type, signal type, separation width, and bike volume.
在有信号灯的路口骑车闯红灯会增加与机动车碰撞的可能性。探索造成这种行为的原因可以提高预测闯红灯概率的准确性，并制定减少此类行为的对策。然而，个体在闯红灯时可能存在未被观察到的异质性，这使得准确预测变得更加具有挑战性。传统模型假设因素参数是固定的，无法捕捉对闯红灯行为的变化影响。在这项研究中，我们采用了全贝叶斯随机参数逻辑回归方法来考虑未被观察到的异质性效应。考虑了两种类型的交叉设施，即有信号灯的路口横道和路段横道。在建模中区分了电动自行车和传统自行车。数据是从中国南京市城区的 16 个横道收集的。研究考察了个体特征、道路几何设计、环境特征和交通变量等因素。模型比较表明，全贝叶斯随机参数逻辑回归方法在统计学上优于标准逻辑回归模型。预测结果显示，在有信号灯的路口横道上，闯红灯的骑车人比在路段横道上更多。影响闯红灯行为的因素包括性别、年龄、自行车类型、道路宽度、中央隔离带的存在、分隔宽度、信号类型、绿灯时间比例、自行车和车辆数量以及平均车速。与未被观察到的异质性相关的因素包括性别、自行车类型、信号类型、分隔宽度和自行车数量。
