基于行人影响的信号交叉口右转车辆跟驰模型

为探究在不同场景下行人对信号交叉口右转车辆跟驰行为的影响，采用无人机采集信号交叉口视频录像，通过视频分析技术提取信号交叉口右转车辆跟驰数据，提出考虑行人过街平均流率影响的右转车辆优化速度函数，构建不同场景下基于行人影响的信号交叉口右转车辆跟驰模型，并对模型进行参数标定和仿真验证.结果表明，保守驾驶员为避免人车冲突会先减速后停车，等待20 s可放行约27人；激进的驾驶员选择在过街行人间隙强行穿越人行横道，穿行前平均速度由13.8 m/s降至8.3 m/s，穿越完成后加速离开，速度波动较小，符合实际情形.

Right Turn Car-following Model of Signalized Intersection Based on Pedestrian Influence

In order to explore the impact of pedestrian influence on the right turn car- following behavior at signalized intersections in different scenarios, UAV was employed to collect video recordings of signalized intersection. And then, the right turn car-following data of signalized intersection was extracted by video analysis technology. Considering the influence of average pedestrian flow rate, a right turn vehicle optimal velocity function was proposed. Thus, a right turn car-following model based on pedestrian influence was built in different scenarios, and the model parameters were calibrated and verified by simulation. The numerical results illustrate that (1) conservative drivers slow down before stopping in order to avoid collision, and about 27 pedestrians can be released after waiting for 20 s; (2) radical drivers choose to force their way across pedestrian crosswalk between pedestrians, and before crossing, the average speed decrease from 13.8 m/s to 8.3 m/s. After the completion of the crossing, the driver accelerate to leave, and the speed fluctuation is smaller, which is more in line with actual situation.