Abstract
Society must achieve net zero carbon emissions to mitigate anthropogenic climate change and preserve a livable planet. Reducing transportation emissions is an important component to achieve net zero because such emissions account for a quarter of global carbon released into the environment. Driven by increasingly available transportation big data and enhanced computational speed, data mining techniques have become powerful tools to achieve transportation decarbonization. This paper describes existing gaps in transportation decarbonization research where data mining can help address problems related to medium and heavy vehicle electrification, electric micromobility safety, and analysis of alternative fuel-powered and plug-in hybrid electric vehicles. Our recommendations encompass open research problems, opportunities for data mining applications, and examples of areas where advancements in data mining techniques are needed. We encourage the data mining community to explore these challenges and opportunities to help achieve net zero emissions goals.
Original language | English (US) |
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Title of host publication | 2023 SIAM International Conference on Data Mining, SDM 2023 |
Publisher | Society for Industrial and Applied Mathematics Publications |
Pages | 953-956 |
Number of pages | 4 |
ISBN (Electronic) | 9781611977653 |
State | Published - 2023 |
Event | 2023 SIAM International Conference on Data Mining, SDM 2023 - Minneapolis, United States Duration: Apr 27 2023 → Apr 29 2023 |
Publication series
Name | 2023 SIAM International Conference on Data Mining, SDM 2023 |
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Conference
Conference | 2023 SIAM International Conference on Data Mining, SDM 2023 |
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Country/Territory | United States |
City | Minneapolis |
Period | 4/27/23 → 4/29/23 |
Bibliographical note
Publisher Copyright:Copyright © 2023 by SIAM.