A Spatio-Temporal Perspective on Commercial Vehicle Travel Patterns in Urban Environments

被引:1
|
作者
Qin, Jianxin [1 ,2 ]
Lin, Yuan [1 ,2 ]
Wu, Tao [1 ,2 ]
Lin, Xinyi [1 ,2 ]
Li, Xiaolong [3 ]
机构
[1] Hunan Normal Univ, Hunan Key Lab Geospatial Big Data Min & Applicat, Changsha 410081, Peoples R China
[2] Hunan Normal Univ, Sch Geog Sci, Changsha 410081, Peoples R China
[3] East China Univ Technol, Key Lab Mine Environm Monitoring & Improving Poya, Minist Nat Resources, Nanchang 330013, Peoples R China
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Urban areas; Trajectory; Matrix decomposition; Global Positioning System; Public transportation; Feature extraction; Data mining; Traffic control; Singular value decomposition; Urban functional area interaction; vehicle travel patterns; spatio-temporal data analysis; singular value decomposition (SVD); SPATIAL STRUCTURE;
D O I
10.1109/ACCESS.2024.3421554
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The relationship between commercial vehicle travel patterns and urban functional areas reveals potential connections between urban form and human geographic flows, which provides critical information for optimizing urban transportation systems. Benefiting from the large-scale trajectory datasets, it would be possible to investigate deeper research by modeling the implied urban travel patterns. This study designs a framework to reveal the collective movement patterns of commercial vehicle trajectories inside the urban environment, focusing on their spatiotemporal variations within functional areas. Stopping behaviors of trajectories were identified to construct spatiotemporal origin-destination (OD) matrices, representing time-varying human geographic flows. The singular value decomposition (SVD) method was employed to quantify spatio-temporal OD matrice to obtain time and space travel features. Travel patterns' dynamics and spatial interactions within functional areas were then analyzed. The experimental results obtained with real-life datasets from Changsha, China, uncovered three typical travel patterns depicting commercial vehicle activities in urban environment shifts from work-related locations on weekdays to leisure destinations on weekends, with central areas experiencing more short and medium-range trips. The findings provide scientific references for optimizing spatio-temporal travel patterns and functional distribution to meet the demands of urban development and traffic management strategies.
引用
收藏
页码:91447 / 91461
页数:15
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