Built environment and public electric vehicle charging: an investigation using POI data and computer vision

Junfeng Jiao and Seung Jun Choi ()
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Junfeng Jiao: The University of Texas at Austin
Seung Jun Choi: The University of Texas at Austin

Public Transport, 2025, vol. 17, issue 2, No 9, 529-563

Abstract: Abstract Public electric vehicle charging stations (EVCSs) are vital for boosting EV adoption. This study investigates Seoul’s public EV charging patterns, taking into account the surrounding urban built environment. We collected built-environment data from land-use maps, Point of Interest (POI) data, and panorama images near public EVCS. The computer-vision technique was used to extract scene features from panorama images. We conducted a spatiotemporal analysis of public EVCS usage. The built-environment factors underwent dimensionality reduction and were assessed for outliers. Descriptive analysis revealed afternoon peak charging times and variations between chargers. Additional peaks are observed in the weekday late evening for chargers located near mega-retail stores. Public EVCS in Seoul were utilized more on weekdays than on weekends. Public EVCS in central business districts saw the most significant usage, with potential cases of overuse. An analysis of the built environment around the chargers showed unique characteristics, with some forming identifiable clusters. The most used public EVCS had more parking areas than other POIs, matching visual observations. Computer visioning mainly recognized highways, parking lots, and crosswalks as common features near the chargers. Outlier test results generally defined fast chargers in the central business district area as outliers. The results also demonstrated that built-environment measures from POI data and computer vision can be used in a complementary manner. Our study offers empirical findings to enhance the understanding of public EV charging usage. We demonstrated the use of POI data and computer-vision techniques to quantify the built environment.

Keywords: EV; EV charging; Built environment; POI; Computer vision (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12469-024-00383-6

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