交通运输研究2026，Vol.12Issue(2)：83-95,13.DOI:10.16503/j.cnki.2095-9931.2026.02.007
基于多维特征融合的服务区小客车载客系数检测方法
A Method for Detecting Passenger Coefficient of Passenger Cars in Service Areas Based on Multi-Dimensional Feature Fusion
摘要
Abstract
In view of the frequent interaction between pedestrians and vehicles,and the lack of consistency in cross category tracking in expressway service areas,which leads to difficulties in passenger coefficient detection,low detection accuracy and poor robustness,a passenger coefficient of passenger cars detection method based on multi-dimensional feature fusion is proposed to improve the accuracy of service area operation data.Firstly,YOLOv10x and ByteTrack are used as detection and tracking modules to build a detection system incorporating multi-dimensional features.Secondly,the Kalman filter method is used to predict the target trajectory and establish the target motion prior,and the classification feature extraction method is introduced to extract the appearance semantic features of vehicles and pedestrians respectively;An adaptive weighted association strategy based on detection confidence is designed,and multi-dimensional feature fusion is carried out.The Hungarian Algorithm is utilized to realize target re-identification(ReID)and matching.Finally,the passenger coefficient is detected based on the monitoring video in the service area,and the algorithm is compared and analyzed by indicators such as tracking accuracy,tracking precision,ID switches,and frame rate.The experimental results show that:the detection method based on multi-dimensional feature fusion can accurately count the passenger coefficient.Compared with the traditional ByteTrack algorithm that relies solely on IOU matching for object tracking,its tracking accuracy and tracking precision are improved by 0.99%and 16.09%,respectively,and the number of ID switches is reduced by 151,representing a 18.33%reduction.This method can significantly improve accuracy and robustness in complex traffic scenes while ensuring inference speed,and can make up for the lack of passenger coefficient statistics in the service area,which can provide a theoretical basis for improving the operation management and service efficiency of the service area,and provide a reference for calculating the actual loading rate on the expressway.关键词
智能交通/载客系数/特征融合/目标检测与跟踪/目标重识别Key words
intelligent transportation/passenger coefficient/feature fusion/target detection and tracking/target re-identification分类
交通工程引用本文复制引用
李洪囤,邬岚,陈超越,王彦,杨明..基于多维特征融合的服务区小客车载客系数检测方法[J].交通运输研究,2026,12(2):83-95,13.基金项目
综合交通运输大数据应用技术交通运输行业重点实验室开放课题(2024B1203) （2024B1203）
江苏省大学生创新训练计划(202410298199Y) （202410298199Y）
