Who are on the road? A study on vehicle usage characteristics based on one-week vehicle trajectory data

作     者：Jihao Deng Yiqing Cui Xiaohong Chen Chris Bachmann Quan Yuan 

作者机构：The Key Laboratory of Road and Traffic EngineeringMinistry of EducationTongji UniversityShanghaiPeople’s Republic of China Department of Civil and Environmental EngineeringUniversity of WaterlooWaterlooPeople’s Republic of China Shanghai Urban Planning and Design Research InstituteShanghaiPeople’s Republic of China 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2023年第16卷第1期

页      面：1962-1984页

核心收录：

学科分类：08[工学] 082304[工学-载运工具运用工程] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程] 

基　　金：supported by the project of the National Natural Science Foundation of China(No.71734004) 

主　　题：Passenger vehicles location identification vehicle classification travel behavior analysis traffic demand management 

摘      要：Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban ***,it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle *** to the advances in data collection and processing techniques,multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular *** this paper,based on a week’s operation data of 6600 passenger cars in Shanghai,we develop a systematic approach for identifying trips and travel purposes,and classify vehicles into four categories using a Gaussian-Mixed-Model.A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of *** that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used *** methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction,but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.