Application of machine learning models to predict driver left turn destination lane choice behavior at urban intersections  

作　　者：Mohammed Moinuddin Logan Proffer Matthew Vechione Aaditya Khanal 

机构地区：[1]The University of Texas at Tyler,Department of Civil Engineering,3900 University Blvd,Tyler,TX 75799,United States [2]The University of Texas at Tyler,Department of Chemical Engineering,3900 University Blvd,Tyler,TX 75799,United States

出　　处：《International Journal of Transportation Science and Technology》2024年第1期155-170,共16页交通科学与技术(英文)

摘　　要：Whenthere are multiple lanes to choose from downstream of a turning movement,drivers should choose the innermost lane so that drivers at other approaches of the intersection may make concurrent turning movements in the outermost lane(s).However,human dri vers do not always choose the innermost lane,which could lead to crashes with other vehi cles.Therefore,predicting human driver behaviors is vital in reducing crashes,as the need to share the roadways with automated vehicles(AVs)continues to grow.In this research,various machine learning models have been used to predict the left turn destination lane choice of human-driven vehicles(HDVs)at urban intersections based on several quantifi able parameters.A total of 174 subject vehicles were extracted and analyzed in Los Angeles,California,and Atlanta,Georgia,using HDV trajectory data from the Next Generation SIMulation(NGSIM)database.Five machine learning techniques,namely bin ary logistic regression,k nearest neighbors,support vector machines,random forest,and adaptive neuro-fuzzy inference system,were applied to the extracted data to predict the lane choice behavior of drivers.The k nearest neighbors model showed the most promising results for the evaluated data with a correct decision score of over 93%for the unseen test data.This model may be programmed into:(i)AVs,in conjunction with sensors,to predict if an HDV is about to turn into the incorrect destination lane;and(ii)microscopic traffic simulation tools so that modelers can identify potential conflicts when HDVs do not select the appropriate destination lane.

关 键 词：Driver Behavior Applied Machine Learning Autonomous Vehicles Destination Lane Choice Safety 

分 类 号：TN9[电子电信—信息与通信工程]