Rail traffic volume estimation based on world development indicators
Апстракт
European transport policy, defined in the White Paper, supports shift from road to rail and waterborne transport. The hypothesis of the paper is that changes in the economic environment influence rail traffic volume. Therefore, a model for prediction of rail traffic volume applied in different economic contexts could be a valuable tool for the transport planners. The model was built using common Machine Learning techniques that learn from the past experience. In the model preparation, world development indicators defined by the World Bank were used as input parameters.
Кључне речи:
rail traffic / prediction / Machine Learning / World Bank / development indicatorsИзвор:
Facta universitatis - series: Mechanical Engineering, 2015, 13, 2, 133-141Издавач:
- Univerzitet u Nišu, Niš
Финансирање / пројекти:
- Истраживање техничко-технолошке, кадровске и организационе оспособљености Железница Србије са аспекта садашњих и будућих захтева Европске Уније (RS-MESTD-Technological Development (TD or TR)-36012)
- Research of technical-technological, staff and organizational capacity of Serbian Railways
Институција/група
GraFarTY - JOUR AU - Lazarević, Luka AU - Kovačević, Miloš AU - Popović, Zdenka PY - 2015 UR - https://grafar.grf.bg.ac.rs/handle/123456789/713 AB - European transport policy, defined in the White Paper, supports shift from road to rail and waterborne transport. The hypothesis of the paper is that changes in the economic environment influence rail traffic volume. Therefore, a model for prediction of rail traffic volume applied in different economic contexts could be a valuable tool for the transport planners. The model was built using common Machine Learning techniques that learn from the past experience. In the model preparation, world development indicators defined by the World Bank were used as input parameters. PB - Univerzitet u Nišu, Niš T2 - Facta universitatis - series: Mechanical Engineering T1 - Rail traffic volume estimation based on world development indicators EP - 141 IS - 2 SP - 133 VL - 13 UR - https://hdl.handle.net/21.15107/rcub_grafar_713 ER -
@article{ author = "Lazarević, Luka and Kovačević, Miloš and Popović, Zdenka", year = "2015", abstract = "European transport policy, defined in the White Paper, supports shift from road to rail and waterborne transport. The hypothesis of the paper is that changes in the economic environment influence rail traffic volume. Therefore, a model for prediction of rail traffic volume applied in different economic contexts could be a valuable tool for the transport planners. The model was built using common Machine Learning techniques that learn from the past experience. In the model preparation, world development indicators defined by the World Bank were used as input parameters.", publisher = "Univerzitet u Nišu, Niš", journal = "Facta universitatis - series: Mechanical Engineering", title = "Rail traffic volume estimation based on world development indicators", pages = "141-133", number = "2", volume = "13", url = "https://hdl.handle.net/21.15107/rcub_grafar_713" }
Lazarević, L., Kovačević, M.,& Popović, Z.. (2015). Rail traffic volume estimation based on world development indicators. in Facta universitatis - series: Mechanical Engineering Univerzitet u Nišu, Niš., 13(2), 133-141. https://hdl.handle.net/21.15107/rcub_grafar_713
Lazarević L, Kovačević M, Popović Z. Rail traffic volume estimation based on world development indicators. in Facta universitatis - series: Mechanical Engineering. 2015;13(2):133-141. https://hdl.handle.net/21.15107/rcub_grafar_713 .
Lazarević, Luka, Kovačević, Miloš, Popović, Zdenka, "Rail traffic volume estimation based on world development indicators" in Facta universitatis - series: Mechanical Engineering, 13, no. 2 (2015):133-141, https://hdl.handle.net/21.15107/rcub_grafar_713 .