EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 235 (2018) MATEC Web Conf., 235 (2018) 00037 References
Open Access
Issue
MATEC Web Conf.
Volume 235, 2018
Horizons of Railway Transport 2018, 10th Year of International Scientific Conference
Article Number 00037
Number of page(s) 6
DOI https://doi.org/10.1051/matecconf/201823500037
Published online 21 November 2018
  1. A. Viswanathan, ‘Data driven analysis of usage and driving parameters that affect fuel consumption of heavy vehicles’, Dissertation, http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A634988&dswid=851 (2013) [Google Scholar]
  2. K. Barry, Rigid Roads Can Reduce Fuel Consumption, VIRED, Gear (2012) [Google Scholar]
  3. A. Geiger A. Holló, A Thernesz, R. Durgo S. Czibor, L. Bartha, P. Gergó Chemically Stabilized Rubber Bitumen, EEC, 1-8. (2012). [Google Scholar]
  4. W. Gołębiewski., T. Stoeck TEKA. Commission of Mot. & Energ. in Agriculture - 2014. 14, 3, 17-24 (2014) [Google Scholar]
  5. Z. Kan, L. Tang, M.P. Kwan, X. Zhang. Int. J. Environ. Res. Public Health 2018, 15, 566; (2018) DOI: 10.3390/ijerph15040566 (2018) [Google Scholar]
  6. O. Franzese, Freight Trucks, 8 (2011) https://info.ornl.gov/sites/publications/files/Pub33386.pdf [Google Scholar]
  7. Hybrid and Battery Electric Vehicles, http://autocaat.org/Technologies/Hybrid_and_Battery_Electric_Vehicles/ [Google Scholar]
  8. X. Zhigang W. Tao, E. Said, Z. Xiangmo, Q. Xiaobo, Computer-Aided Civil and Infrastructure Engineering,. 33, pp. 209, (2018.) [CrossRef] [Google Scholar]
  9. H. Almér, Machine learning and statistical analysis in fuel consumption prediction for heavy vehicles in KTH, Sweden, (2015) [Google Scholar]
  10. F. Perrotta, T. Parry, L. Neves,. IEEE Conference on Big Data (2017). DOI: 10.1109/BigData.2017.8258382.2017 [Google Scholar]
  11. S. Wickramanayake, D. Bandara, Fuel consumption prediction of fleet vehicles using Machine Learning: A comparative study. 90-95. 10.1109/MERCon.2016.7480121. (2016). [Google Scholar]
  12. J. Telberg Fixed Route Optimal Refuelling Plan. KTH Royal Institute of Technology. www.diva-portal.org/smash/get/diva2:957311/FULLTEXT01.pdf [Google Scholar]
  13. B. Predić, M. Madić, M. Roganović, M. Kovačević, D. Stojanović, Facta Universitatis, Series: Automatic Control and Robotics, 1 (2), 105-116. Retrieved from http://casopisi.junis.ni.ac.rs/index.php/FUAutContRob/article/view/1597 (2016). [Google Scholar]
  14. M. Højsleth, A. T. Lau, A. Strandfelt, Personalized Route Planning with Recharging for Electric Vehicles. https://projekter.aau.dk/projekter/files/239555254/article.pdf [Google Scholar]
  15. A. Török, A. Török, F. Heinitz, Transport and Telecommunication Journal. 15 (4), 292-298. https://doi.org/10.2478/ttj-2014-0025 (2014). [CrossRef] [Google Scholar]
  16. R. Ildarkhanov, Periodica Polytechnica Transportation Engineering. Onlinefirst, 7 DOI 10.3311/PPtr.10553 (2018) [Google Scholar]
  17. Z. Szalay, T. Tettamanti, D- Esztergár-Kiss, I. Varga, C. Bartolini, Periodica Polytechnica Transportation Engineering, 46 (1), 29-35. DOI: 10.3311/PPtr.10753. (2018) [CrossRef] [Google Scholar]
  18. P. Conradi, P. Bouteiller, S. Hanßen, Advanced Microsystemsfor Automotive Applications, Heidelberg:Springer,. 269-277, (2011) [Google Scholar]
  19. G. De Nunzio, L. Thibault, A. Sciarretta, Intelligent Transportation Systems (ITSC) 2017 IEEE 20th International Conference, 1-7, (2017) [Google Scholar]
  20. A. Rimkus, J. Matijošius, M. Bogdevičius, Á. Bereczky, Á. Török,. Energy, 152, 640-651. (2018) [CrossRef] [Google Scholar]
  21. I. Barabás. Fuel, 109, 563-574. (2013) [CrossRef] [Google Scholar]
  22. D. Dobrowolski, P. Droździel, R. Madleňák, I. K. Rybicka, D. Siłuch, Advances in Science and Technology. Research Journal, 12 (3). (2018) [Google Scholar]
  23. R. Madleňák, L. Madleňáková, D. Hoštáková, P. Drozdziel, Á. Török, Advances in Science and Technology. Research Journal, 12 (2). (2018). [Google Scholar]
  24. L. Hansut, A. David, J. Gasparik. Bussines Logistic In Modern Management. 29-42. (2017) [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.