Open Access
| Issue |
E3S Web Conf.
Volume 729, 2026
1st Sustainable Power, Energy, Transportation, and Materials Conference (SPETM 2026)
|
|
|---|---|---|
| Article Number | 04003 | |
| Number of page(s) | 8 | |
| Section | Electromobility & Transportation | |
| DOI | https://doi.org/10.1051/e3sconf/202672904003 | |
| Published online | 31 July 2026 | |
- D. Li, A.D. Lau, Y. Gong, Electric vehicles empowering the construction of green sustainable transportation networks in Chinese cities, Dynamic evolution frontier trends and construction pathways, Energies 18, 8 (2025) 1943. [Google Scholar]
- B. Srinivasan, Predicting future electric vehicle sales, A time series forecasting approach using historical EV sales data, International journal for applied information management 5, 3 (2025) 177189. [Google Scholar]
- G. Han, et. al., A structured review of electric vehicle sales research, Multi-level driving factors and forecasting pathways over the past decade, World electric vehicle journal 17, 3, (2026) 122. [Google Scholar]
- A. Ajiono, T. Hariguna, Comparison of three time series forecasting methods on linear regression, Exponential smoothing and weighted moving average, International Journal of Informatics and Information Systems 6, 2 (2023) 89–102. [Google Scholar]
- S.K. Shil, M.S. Chowdhury, N.R. Tannier, M.T. Tarafder, R. Akter, N. Gurung, M.M. Sizan, Forecasting electric vehicle adoption in the USA using machine learning models, Journal of computer science and technology studies 6, 5 (2024) 61–74. [Google Scholar]
- S. Dirir, Predictive modeling of electric vehicle adoption in OECD nations, An empirical assessment, Quality & quantity (2026) DOI: 10.1007/s11135-y [Google Scholar]
- S. Veysi, M. Moshfeghi, A. Sadrfaridpour, P. Emamy, Electric vehicle sales forecast for the UK, Integrating machine learning time Series Models and global trends, Algorithms 18, 7 (2025) 430 DOI: 10.3390/a18070430. [Google Scholar]
- S. Afandizadeh, D. Sharifi, N. Kalantari, H. Mirzahossein, Using machine learning methods to predict electric vehicles penetration in the automotive market, Sci. rep. (2023) doi: 10.1038/s41598-023-35366-3. [Google Scholar]
- C. Bentéjac, A. Csörgo, G. Martinez-Munoz, A comparative analysis of gradient boosting algorithms, Artificial Intelligence Review 54 (2021) 1937–1967. [Google Scholar]
- T. Chen, C. Guestrin, X GBoost, A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining San Francisco CA ACM (2016) 785–794. [Google Scholar]
- L. Maybury, P. Corcorana, L. Cipcigan, Mathematical modelling of electric vehicle adoption, A systematic literature review, Transport and environment 107 (2022) 103278. [Google Scholar]
- M.P. Clements, P.H. Franses, and N.R. Swanson, Forecasting economic and financial time series with non-linear models, International journal of forecasting 20, 2 (2004) 169–183. [Google Scholar]
- F.L. Gewers et al., Principal component analysis, A natural approach to data exploration, ACM Computing Surveys CSUR (2021) 1–34. [Google Scholar]
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