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
I. Tomov (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Issa K. Hanou – Mentor (TU Delft - Algorithmics)
M.M. de Weerdt – Mentor (TU Delft - Algorithmics)
Jing Sun – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
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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.