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.
Data records from train describer systems are a valuable source of information for analysing railway operations performance and assessing railway timetable quality. This paper presents a process mining tool based on event data records from the Dutch train describer system TROTS, including algorithms developed for the automatic identification of route conflicts with conflicting trains, arrival and departure times/delays at stations, and train paths on track section and blocking time level. Visualisations of the time-distance diagrams and blocking time diagrams support the analysis of incidents, track obstructions, disruptions, and structural errors in the timetable design.
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