In many Chinese cities,motorized vehicles （M-vehicles） move slowly at intersections due to the interference of a large number of non-motorized vehicles （NM-vehicles）.The slow movement makes a part of M-vehicles fa...In many Chinese cities,motorized vehicles （M-vehicles） move slowly at intersections due to the interference of a large number of non-motorized vehicles （NM-vehicles）.The slow movement makes a part of M-vehicles fail to leave intersections timely after the traffic signal tums red,and thereby conflicts between vehicles from two directions occur.The phenomenon was analyzed graphically by using the cumulative vehicle curve.Delays in three cases were modeled and compared：NM-vehicle priorities and M-vehicle priorities with all-red intervals unable to release all vehicles,and longer all-red intervals ensuring release all vehicles.Marginal delays caused by two illegal behaviors that occasionally happened in mixed traffic intersections were also investigated.It is concluded that increasing the speed of M-vehicles leaving intersections and postponing the entering of NM-vehicles are the keys in mathematics,although they are uneasy in disordered mixed traffic intersections due to a dilemma between efficiency and orders in reality.The results could provide implications for the traffic management in the cities maintaining a large number of M-and NM-vehicles.展开更多
【目的】对城市交叉口采用的左转非机动车信号灯设施进行交通安全性量化评估。【方法】提出一种基于拓展碰撞时间(extended time to collision,ETTC)指标的左转非机动车信号灯安全效应评估方法。针对现有的碰撞时间(time to collision,T...【目的】对城市交叉口采用的左转非机动车信号灯设施进行交通安全性量化评估。【方法】提出一种基于拓展碰撞时间(extended time to collision,ETTC)指标的左转非机动车信号灯安全效应评估方法。针对现有的碰撞时间(time to collision,TTC)指标不适于评估交叉口左转非机动车冲突的问题,考虑非机动车车辆尺寸与加速度对交通冲突的影响,采用拓展碰撞时间指标,评估交叉口非机动车交通冲突。收集长沙市4个信号交叉口的视频大数据,利用视频软件Tracker提取车辆微观轨迹后,开展案例分析。【结果】左转非机动车信号灯在时间上明确了非机动车的通行权,其设置能显著降低非机动车冲突率,在平峰、高峰时段非机动车冲突率分别降低了40.11%、25.27%。在直行相位末期、左转相位即将启亮时,设置组的左转非机动车在待行区等待,冲突率降为0;而对比组近50%的非机动车违规左转,冲突严重。设置左转非机动车信号灯的改善效果随非机动车流量的增大呈先增加后降低趋势,而随机动车流量的增大呈逐步波动下降趋势。【结论】本研究揭示了非机动车左转信号灯的设置对减少交叉口交通冲突的影响,可为城市交叉口非机动车交通安全管控提供有益参考。展开更多
为探究城市信号交叉口影响人车冲突严重程度的关键因素,提升交叉口安全管理水平,本文选取典型的城市道路信号交叉口,采用无人机航拍获取交通流视频,基于人工观测和Tracker软件解析处理得到冲突点信息参数与位置分布特征。为量化冲突程度...为探究城市信号交叉口影响人车冲突严重程度的关键因素,提升交叉口安全管理水平,本文选取典型的城市道路信号交叉口,采用无人机航拍获取交通流视频,基于人工观测和Tracker软件解析处理得到冲突点信息参数与位置分布特征。为量化冲突程度,采用后侵入时间、冲突区域车速、潜在碰撞距离作为人车冲突严重程度评价指标,利用K-means聚类算法将过街冲突按严重程度迭代分类,确定人、车、路三方面下的21个解释变量。通过Pearson相关性分析筛选,建立多元有序Logistic模型,并通过ROC(Receiver Operating Characteristic)曲线验证得到模型对冲突严重级别的估计分类概率结果AUC(Area Under Curve)为0.971。结果表明:行人与冲突点的距离(0.364)、车辆在冲突点前的趋向(停车让行为-4.22,减速让行为-0.937)、行人是否闯红灯行为(0.818)、机动车道数量(0.29)、行人等待红灯时间长短(0.012)、行人年龄段(-0.869)、行人着装颜色(0.673)是影响人车冲突严重程度的显著因素。本文研究结果能够为行人过街安全的交通策略制定提供一定参考价值。展开更多
基金Project(2012CB725403)supported by the National Key Research Program of ChinaProject(71131001)supported by the National Natural Science Foundation of ChinaProject(2012JBM064)supported by the Fundamental Research Funds for the Central Universities of China
文摘In many Chinese cities,motorized vehicles （M-vehicles） move slowly at intersections due to the interference of a large number of non-motorized vehicles （NM-vehicles）.The slow movement makes a part of M-vehicles fail to leave intersections timely after the traffic signal tums red,and thereby conflicts between vehicles from two directions occur.The phenomenon was analyzed graphically by using the cumulative vehicle curve.Delays in three cases were modeled and compared：NM-vehicle priorities and M-vehicle priorities with all-red intervals unable to release all vehicles,and longer all-red intervals ensuring release all vehicles.Marginal delays caused by two illegal behaviors that occasionally happened in mixed traffic intersections were also investigated.It is concluded that increasing the speed of M-vehicles leaving intersections and postponing the entering of NM-vehicles are the keys in mathematics,although they are uneasy in disordered mixed traffic intersections due to a dilemma between efficiency and orders in reality.The results could provide implications for the traffic management in the cities maintaining a large number of M-and NM-vehicles.
文摘【目的】对城市交叉口采用的左转非机动车信号灯设施进行交通安全性量化评估。【方法】提出一种基于拓展碰撞时间(extended time to collision,ETTC)指标的左转非机动车信号灯安全效应评估方法。针对现有的碰撞时间(time to collision,TTC)指标不适于评估交叉口左转非机动车冲突的问题,考虑非机动车车辆尺寸与加速度对交通冲突的影响,采用拓展碰撞时间指标,评估交叉口非机动车交通冲突。收集长沙市4个信号交叉口的视频大数据,利用视频软件Tracker提取车辆微观轨迹后,开展案例分析。【结果】左转非机动车信号灯在时间上明确了非机动车的通行权,其设置能显著降低非机动车冲突率,在平峰、高峰时段非机动车冲突率分别降低了40.11%、25.27%。在直行相位末期、左转相位即将启亮时,设置组的左转非机动车在待行区等待,冲突率降为0;而对比组近50%的非机动车违规左转,冲突严重。设置左转非机动车信号灯的改善效果随非机动车流量的增大呈先增加后降低趋势,而随机动车流量的增大呈逐步波动下降趋势。【结论】本研究揭示了非机动车左转信号灯的设置对减少交叉口交通冲突的影响,可为城市交叉口非机动车交通安全管控提供有益参考。
文摘为探究城市信号交叉口影响人车冲突严重程度的关键因素,提升交叉口安全管理水平,本文选取典型的城市道路信号交叉口,采用无人机航拍获取交通流视频,基于人工观测和Tracker软件解析处理得到冲突点信息参数与位置分布特征。为量化冲突程度,采用后侵入时间、冲突区域车速、潜在碰撞距离作为人车冲突严重程度评价指标,利用K-means聚类算法将过街冲突按严重程度迭代分类,确定人、车、路三方面下的21个解释变量。通过Pearson相关性分析筛选,建立多元有序Logistic模型,并通过ROC(Receiver Operating Characteristic)曲线验证得到模型对冲突严重级别的估计分类概率结果AUC(Area Under Curve)为0.971。结果表明:行人与冲突点的距离(0.364)、车辆在冲突点前的趋向(停车让行为-4.22,减速让行为-0.937)、行人是否闯红灯行为(0.818)、机动车道数量(0.29)、行人等待红灯时间长短(0.012)、行人年龄段(-0.869)、行人着装颜色(0.673)是影响人车冲突严重程度的显著因素。本文研究结果能够为行人过街安全的交通策略制定提供一定参考价值。
