NDLI: Predictive modelling of running and dwell times in railway traffic

Content Provider	Springer Nature Link
Author	Kecman, Pavle Goverde, Rob M. P.
Copyright Year	2015
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.
Starting Page	295
Ending Page	319
Page Count	25
File Format	PDF
ISSN	1866749X
Journal	Public Transport
Volume Number	7
Issue Number	3
e-ISSN	16137159
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-06-04
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Predictive modelling Robust regression Running and dwell times Tree-based models Operation Research/Decision Theory Automotive Engineering Computer-Aided Engineering (CAD, CAE) and Design Transportation
Content Type	Text
Resource Type	Article
Subject	Management Science and Operations Research Information Systems Transportation Mechanical Engineering

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