Ensuring Timetable Stability With Train
Traffic Data
Author(s)
T. Graffagnino
Abstract
Swiss Federal Railways plan and operate one of the densest rail networks in the
world. Over the last 8 years, punctuality on the network has been steadily
increasing. Figures comparing train traffic density and showing the punctuality
of Swiss trains over the last 8 years will be presented and briefly discussed. Then
the process of collecting the train traffic data at SBB will be explained and
discussed.
The main points of this paper will be the mathematical methods and the
graphical data visualisation method developed by SBB to continually optimise
the timetable with extensive use of historical train traffic data.
Based on quite simple collected data – actual arrival and arrival delay, actual
departure and departure delay – for each train at about 1000 stations, the paper
will define and review the different types of analysis that the method facilities:
arrival punctuality, departure punctuality, dwelling time, journey time, dwelling
time deviation and journey time deviation.
The paper will also discuss the type of distribution function which can be
observed and explain why the original statistical definition of boxplots by John
Wilder Tukey (Exploratory Data Analysis, Addison-Wesley) has been adapted
for rail-related use. Subsequently, the strength of this new defined boxplot is
demonstrated with a top-down delay cause analysis starting with punctuality in
Switzerland, then comparing the major Swiss stations and the train family, and
finally identifying the problems on the corridors. We then show how the usage of
median insights into the dynamics of the railway system helps us to identify
potential stability problems.
To conclude, the identification and visualisation of knock-on delay between
trains based on actual train traffic data is addressed as an interesting open issue.
Keywords: timetable stability, traffic data, statistic, boxplot, punctuality, cyclic
timetable, data mining.
Keywords
timetable stability, traffic data, statistic, boxplot, punctuality, cyclictimetable, data mining
Related Book
Other papers in this volume
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