Learning Patterns in Train Position Data

Automatic Detection of Whether a Solution of the Train Unit Shunting Problem (TUSP) is a Week or a Weekend Day

Bachelor thesis (2024)

Authors

I. Tomov Electrical Engineering, Mathematics and Computer Science

Contributors

I.K. Hanou Algorithmics - EEMCS (mentor)
M.M. de Weerdt Algorithmics - EEMCS (mentor)
J. Sun Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science
Planning Train shunting Binary classification Pattern extraction
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Published Date

23-06-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

When not in service, trains are parked and serviced at shunting yards. The Train Unit Shunting Problem (TUSP), an NP-hard problem, encompasses the challenge of planning movements and tasks in shunting yards. A feasible shunting plan serves as a solution to the TUSP. Current automated planning tools utilized to assist human planners in this computationally heavy planning task are not able to distinguish inherent patterns in input train data, as opposed to humans. This paper aims to address this technological gap by examining whether valuable patterns could be extracted from shunting plan data, consisting of solutions to the TUSP. More specifically, it is mainly concerned with the automatic detection of whether a solution to the TUSP is a week or a weekend day. Therefore, the data is examined for the presence of several groups of patterns. Moreover, binary classification is performed on the data. The experiments conducted in this study suggest the presence of valuable patterns in the data, which could be leveraged to design specialized heuristics for automated planning models tailored to generate shunting plans for weekdays and weekends.