EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 363 (2022) E3S Web Conf., 363 (2022) 04047 References
Open Access
Issue
E3S Web Conf.
Volume 363, 2022
XV International Scientific Conference on Precision Agriculture and Agricultural Machinery Industry “State and Prospects for the Development of Agribusiness - INTERAGROMASH 2022”
Article Number 04047
Number of page(s) 9
Section Environmental Education and Digital Solutions. Environmentally Responsible Behavior
DOI https://doi.org/10.1051/e3sconf/202236304047
Published online 14 December 2022
  1. A. Taeihagh, H.S.M. Lim, Transport reviews 39(1), 103–128 (2019). DOI10.1080/01441647.2018.1494640. [CrossRef] [Google Scholar]
  2. T. Litman, Autonomous vehicle implementation predictions: Implications for transport planning (2020). [Google Scholar]
  3. United Nations. Department of Economic and Social Affairs. World youth report: Youth and the 2030 agenda for sustainable development (United Nations Publications, 2018). [Google Scholar]
  4. European Environmental Agency. Total greenhouse gas emission trends and projections in Europe (2021). [Google Scholar]
  5. European Union. EU Transport in Figures (2021). Publications Office of the European Union. [Google Scholar]
  6. European Environmental Agency. Trends and projections in Europe. EEA Report No 13/2021 (2021). [Google Scholar]
  7. S. Hörl, F. Ciari, K.W. Axhausen, Arbeitsberichte Verkehrs-und Raumplanung 1216 (2016). DOI10.3929/ethz-b-000121359. [Google Scholar]
  8. National Highway Traffic Safety Administration et al. National motor vehicle crash causation survey: Report to congress. National Highway Traffic Safety Administration Technical Report DOT HS vol. 811, p. 059 (2008). [Google Scholar]
  9. A. Smirnov, E. Smolokurov, Y. Fir, Transport. Res. Proc. 61, 139–146 (2022). DOI10.1016/j.trpro.2022.01.023. [CrossRef] [Google Scholar]
  10. K. Mouratidis, V.C. Serrano, Transportation research part F: traffic psychology and behaviour 76, 321–335 (2021). DOI10.1016/j.trf.2020.12.007. [CrossRef] [Google Scholar]
  11. E. Fraedrich, et al., Transportation research part A: policy and practice 122, 162–172 (2019). DOI10.1016/j.tra.2018.02.018. [CrossRef] [Google Scholar]
  12. P.M. Bosch, F. Becker, H. Becker, K.W. Axhausen, Transport Policy 64, 76–91 (2018). DOI10.1016/j.tranpol.2017.09.005. [CrossRef] [Google Scholar]
  13. Zh. Dai, X. Cathy Liu, X. Chen, X. Ma, Transport. Res. Part C: Emerging Techn. 114, 598–619 (2020). DOI10.1016/j.trc.2020.03.001. [CrossRef] [Google Scholar]
  14. A. Moradzadeh, K. Khaffafi, Emerging Sci. J. 1(4), 201–207 (2017). DOI10.28991/ijse-01123. [Google Scholar]
  15. S. Nordhoff, M. Kyriakidis, Bart van Arem, R. Happee, Theoret. issues in ergon. sci. 20(6), 682–710 (2019). DOI10.1080/1463922X.2019.1621406. [CrossRef] [Google Scholar]
  16. M.E. López-Lambas, A. Alonso, Sustainability 11(18), 4986 (2019). DOI10.3390/su11184986. [CrossRef] [Google Scholar]
  17. A.O. Salonen, N. Haavisto, Sustainability 11(3), 588 (2019). DOI10.3390/su11030588. [CrossRef] [Google Scholar]
  18. C. Goldbach, et al., Transport. Res. Interdisciplinary Perspectives 13, 100504 (2022). DOI10.1016/j.trip.2021.100504. [CrossRef] [Google Scholar]
  19. P.M. Smolnicki, J. Sołtys, Procedia eng. 161, 2184–2190 (2016). DOI 10.1016/j.proeng.2016.08.813. [CrossRef] [Google Scholar]
  20. S. Sachan, N. Kishor, Optimal location for centralized charging of electric vehicle in distribution network in 18th Mediterranean Electrotechnical Conference (MELECON) (2016). DOI:10.1109/MELCON.2016.7495326. [Google Scholar]
  21. B.A. Sandén, Systems Perspectives on Electromobility 2013 (2013). [Google Scholar]
  22. S.P.S. Badwal, S.S. Giddey, Ch. Munnings, A.I. Bhattand, A.F. Hollenkamp, Frontiers in chemistry 2, 79 (2014). DOI10.3389/fchem.2014.00079. [Google Scholar]
  23. National Research Council et al. The Role of the Chemical Sciences in Finding Alternatives to Critical Resources: A Workshop Summary (2012). [Google Scholar]
  24. M. Bojarski, et al., End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316. – (2016). [Google Scholar]
  25. H.J. Vishnukumar, C. Müller, B. Butting, E. Sax, Machine learning and deep neural network—Artificial intelligence core for lab and real-world test and validation for ADAS and autonomous vehicles: AI for efficient and quality test and validation in Intelligent Systems Conference (IntelliSys), IEEE, p. 714–721 (2017). DOI10.1109/INTELLISYS.2017.8324372. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.