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Characterizing mobility patterns of private electric vehicle users with trajectory data.

Authors :
Yang, Xiong
Zhuge, Chengxiang
Shao, Chunfu
Huang, Yuantan
Hayse Chiwing G. Tang, Justin
Sun, Mingdong
Wang, Pinxi
Wang, Shiqi
Source :
Applied Energy. Sep2022, Vol. 321, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

• Characterizing Electric Vehicle (EV) users' mobility patterns using trajectory data. • Exploring the association between mobility patterns and the built environment. • Private EV users tended to have regular travel and activity patterns. • EV users performed activities at a small number of places and within a small area. • Gymnasia tended to be more statistically associated with the mobility patterns. Human mobility pattern analysis has received rising attention. However, little is known about the mobility patterns of private Electric Vehicle (EV) users. In response, this paper characterized mobility patterns of private EV users using a unique one-month dataset containing moving trajectories of 76,774 actual private EVs in January 2018 in Beijing. Specifically, we first explored the diversity, regularity, spatial extent, and uniqueness of EV users' mobility patterns. The results suggested that most EV users had both regular travel and activity patterns (the mean travel and activity entropies were 2.17 and 1.83, respectively) with special preferences towards some specific activity locations relative to all the locations they visited (the mean number of activity locations visited was 13.57 in one month). Furthermore, they tended to perform activities within a small geographical area (the mean radius of gyration was 7.60 km) and have a short daily travel distance (the mean value was 37.35 km) relative to their electric driving range. Further, we associated EV users' mobility patterns with the built environment through ordinary least squares and geographically weighted regression models, particularly considering the so-called modifiable areal unit problem (MAUP). Due to the MAUP, most of the statistically significant built environment variables varied across spatial analysis units (SAUs). Gymnasia was the only variable statistically associated with the mobility patterns for all SAUs; while the variables related to residence and workplace were not statistically associated. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03062619
Volume :
321
Database :
Academic Search Index
Journal :
Applied Energy
Publication Type :
Academic Journal
Accession number :
157524024
Full Text :
https://doi.org/10.1016/j.apenergy.2022.119417