基于航拍数据的异质车流跟驰模型参数辨识与特征分析OA

This study uses the characteristics of high precision and wide range of UAV aerial photography technology to acquire high-precision video data of vehicle following behavior in urban traffic flow,which aims to gain a deeper understanding of the influence of multiple factors on the characteristics of vehicle following behavior by identifying the parameters of the microscopic following model.The study collects traffic data under different road conditions in Xi'an,and adopts advanced computer vision algorithms SIFT and YOLOv5+DeepSort to realize automatic identification and trajectory tracking of different types of vehicles.Use the extracted tracking data to identify the parameters of the full velocity difference(FVD)model,and correlate the identified parameter values with vehicle models and traffic states,so as to quantitatively analyze the effects of these factors on drivers′ traffic following behaviors.The results of the study reveal that there are significant differences in the parameters of the following model across vehicle models and road conditions,which implies the adaptive adjustment of drivers′behavior strategies in the case of different traffic flows and vehicle types.This study provides new perspectives and methods for understanding and modeling human-vehicle interaction behavior in complex traffic environments on the parameter level.