摘要
以城市快速路互通交织区换道行为为对象,研究换道过程中换道持续距离的选择行为.以实测车行轨迹数据为基础,利用因果推断理论识别影响换道持续距离选择的主要因素:目标车辆换道前后的速度和换道持续时间、当前车道和目标车道前后车的间距.分别利用支持向量机模型和深度学习模型进行换道持续距离选择行为建模,检验换道持续距离选择行为影响因素分析的有效性.结果表明,经筛选后的影响因素提高了行为选择模型的预测速度以及深度学习模型的预测精度;支持向量模型虽然预测速度更快,但预测精度不如深度学习模型.对典型换道行为进行特征分析,为城市快速路互通交织区管理方案的制定奠定了理论基础,是对换道过程行为特征研究的有效补充,精确刻画了换道行为过程.
The selection behavior of the lane-changing distance in the lane-changing process was examined by taking the lane-changing behavior in the urban expressway interchange weaving section as an object.Utilizing measured vehicle trajectory data,causal inference theory was applied to identify key factors influencing the choice of lanechanging distance.The factors included the velocity and lane-changing duration of the target vehicle before and after lane-changing,as well as the distance between the current lane and the vehicles in front and behind the target lane.The support vector model and the deep learning model were employed respectively to establish models for lanechanging distance selection behavior to test the validity of the influencing factor analysis.Results show that the screened factors contribute to enhancing the prediction speed of the behavioral choice model and improving the prediction accuracy of the deep learning model.While the support vector model offers faster predictions,it falls behind in prediction accuracy compared to the deep learning model.Conducting characteristic analysis on typical lane-changing behaviors provided a foundational basis for formulating management strategies in the urban expressway interchange weaving section,effectively supplementing existing research on lane-changing behavioral characteristics and offering a nuanced portrayal of the lane-changing process.
作者
赵顗
安醇
李铭浩
马健霄
怀硕
ZHAO Yi;AN Chun;LI Minghao;MA Jianxiao;HUAI Shuo(College of Automobile and Traffic Engineering,Nanjing Forestry University,Nanjing 210037,China;School of Traffic and Transportation,Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《浙江大学学报(工学版)》
北大核心
2025年第1期205-212,共8页
Journal of Zhejiang University:Engineering Science
基金
国家自然科学基金资助项目(62303228)
教育部人文社会科学研究项目(23YJC630253).
关键词
城市快速路互通交织区
换道持续距离
因果推断
支持向量机
深度学习
urban expressway interchange weaving section
lane-changing distance
causal inference
support vector machine
deep learning
