Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6936766 | Transportation Research Part C: Emerging Technologies | 2015 | 12 Pages |
Abstract
We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Nikola MarkoviÄ, Sanjin MilinkoviÄ, Konstantin S. Tikhonov, Paul Schonfeld,