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Web Mining to Inform Locations of Charging Stations for Electric Vehicles

Authors:

Philipp Hummler,

Christof Naumzik,

Stefan FeuerriegelAuthors Info & Claims

WWW '22: Companion Proceedings of the Web Conference 2022

Pages 166 - 170

https://doi.org/10.1145/3487553.3524264

Published: 16 August 2022 Publication History

Abstract

The availability of charging stations is an important factor for promoting electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for city planners, the crucial question is where to place charging stations so that they reach a large utilization. Here, we hypothesize that the utilization of EV charging stations is driven by the proximity to points-of-interest (POIs), as EV owners have a certain limited willingness to walk between charging stations and POIs. To address our research question, we propose the use of web mining: we characterize the influence of different POIs from OpenStreetMap on the utilization of charging stations. For this, we present a tailored interpretable model that takes into account the full spatial distributions of both the POIs and the charging stations. This allows us then to estimate the distance and magnitude of the influence of different POI types. We evaluate our model with data from approx. 300 charging stations and 4,000 POIs in Amsterdam, Netherlands. Our model achieves a superior performance over state-of-the-art baselines and, on top of that, is able to offer an unmatched level of interpretability. To the best of our knowledge, no previous paper has quantified the POI influence on charging station utilization from real-world usage data by estimating the spatial proximity in which POIs are relevant. As such, our findings help city planners in identifying effective locations for charging stations.

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Cited By

Lin FHsieh H(2024)A Hierarchy-Aware Approach to Cross-Region Spatial-Temporal Inference of Unarchived Event in Urban Mobility InfrastructureDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_14(214-224)Online publication date: 1-Oct-2024
https://doi.org/10.1007/978-981-97-5552-3_14
Elattar Hvon Tüllenburg FKaras SValdes J(2024)An Open-Source Model for Estimating the Need to Expansion in Local Charging InfrastructuresGeographical Information Systems Theory, Applications and Management10.1007/978-3-031-60277-1_5(69-91)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-60277-1_5

Index Terms

Web Mining to Inform Locations of Charging Stations for Electric Vehicles

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Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '22: Companion Proceedings of the Web Conference 2022

April 2022

1338 pages

ISBN:9781450391306

DOI:10.1145/3487553

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Lionel Médini
Université Lyon 1, France
,
Ivan Herman
W3C / retired

Copyright © 2022 ACM.

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New York, NY, United States

Publication History

Published: 16 August 2022

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WWW '22

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Lin FHsieh H(2024)A Hierarchy-Aware Approach to Cross-Region Spatial-Temporal Inference of Unarchived Event in Urban Mobility InfrastructureDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_14(214-224)Online publication date: 1-Oct-2024
https://doi.org/10.1007/978-981-97-5552-3_14
Elattar Hvon Tüllenburg FKaras SValdes J(2024)An Open-Source Model for Estimating the Need to Expansion in Local Charging InfrastructuresGeographical Information Systems Theory, Applications and Management10.1007/978-3-031-60277-1_5(69-91)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-60277-1_5

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