Analyzing passenger train arrival delays with support vector regression
place - europe, mode - rail, infrastructure, technology
Train arrival delays, Support vector regression, Artificial neural networks, Machine learning, Infrastructure, Statistics
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.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Marković, N., Milinković, S., Tikhonov, K.S., & Schonfeld, P. (2015). Analyzing passenger train arrival delays with support vector regression. Transportation Research Part C: Emerging Technologies, Vol. 56, pp. 251–262.