Predictive modelling of running and dwell times in railway traffic
Document Type
Journal Article
Publication Date
2015
Subject Area
mode - rail, infrastructure - track, infrastructure - station, planning - methods
Keywords
Predictive modelling, Robust regression, Running and dwell times, Tree-based models
Abstract
Accurate estimation of running and dwell times is important for all levels of planning and control of railway traffic. The availability of historical track occupation data with a high degree of granularity inspired a data-driven approach for estimating these process times. In this paper we present and compare the accuracy of several approaches to model running and dwell times in railway traffic. Three global predictive model approaches are presented based on advanced statistical learning techniques: LTS robust linear regression, regression trees and random forests. Also local models are presented for a particular train line, station or block section, based on LTS robust linear regression with some refinements. The models are validated and compared using a test set independent from the training set. The applicability of the proposed data-driven approach for real-time applications is proved by the accuracy of the obtained estimates and the low computation times. Overall, the local models perform best both in accuracy and computation time.
Rights
Permission to publish the abstract has been given by SpringerLink, copyright remains with them.
Recommended Citation
Kecman, P. & Goverde, R.M.P. (2015). Predictive modelling of running and dwell times in railway traffic. Public Transport, Vol. 7, pp. 295-319.
