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

被引:8
作者
Deng, Jihao [1 ,2 ]
Cui, Yiqing [3 ]
Chen, Xiaohong [1 ]
Bachmann, Chris [2 ]
Yuan, Quan [1 ,4 ]
机构
[1] Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Shanghai, Peoples R China
[2] Univ Waterloo, Dept Civil & Environm Engn, Waterloo, Peoples R China
[3] Shanghai Urban Planning & Design Res Inst, Shanghai, Peoples R China
[4] 4800 Caoan Rd, Shanghai 201804, Peoples R China
基金
中国国家自然科学基金;
关键词
Passenger vehicles; location identification; vehicle classification; travel behavior analysis; traffic demand management; IDENTIFICATION; MODEL; ACCURACY; SYSTEM; SHIFT;
D O I
10.1080/17538947.2023.2218117
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management. However, it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use. Thanks 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 patterns. In 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 vehicles. We find 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 vehicles. The 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.
引用
收藏
页码:1962 / 1984
页数:23
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