基于logistic回归分析的路段人行横道机动车让行研究

为探究道路交通条件对路段人行横道机动车让行的影响，通过无人机视频调查，采集了20处路段人行横道机动车通行过程数据，在对logistic模型应用条件进行检验的基础上，建立了路段人行横道机动车让行概率模型，并检验了模型的准确性。模型分析结果表明，安全间距、车速、监控、车道数、行人数量和行人位置对让行率具有显著影响；通过增设监控和行人中央驻足区可分别提高让行率为原来的3.700倍和4.339倍，当车道数大于4时，即使采取监控措施让行率仍将小于30%，应采用信号控制。

Vehicle Yielding Behavior at Mid-Block Crosswalks Based on Logistic Regression

In order to analyze the factors that affect the vehicle yielding behavior at crosswalk, the impact of road and traffic conditions on the vehicle yielding behavior at mid-block crosswalks was studied based on the field data. Using the Unmanned Aerial Vehicle (UAV), the traffic data of vehicles passing through the crossings at 20 mid-block crosswalks were collected. Based on the tests of the application presuppositions of the logistic model, the vehicle yielding probability model was established. The accuracy of the model was tested. The results of model analysis show that the safety space gap, vehicle speed, monitoring, number of lanes, number of pedestrians, and pedestrian position have significant impact on the yielding rate; by adding monitoring and pedestrian central island, the yielding rate can be increased by 3.700 times and 4.339 times respectively; when the number of lanes is more than 4, the yielding rate is less than 30% even if the monitoring is set, and the signal control should be used in that condition.