Learning Patterns in Train Position Data

Classifying locations by identifying station specific patterns

Bachelor thesis (2024)

Authors

I.Y. Smilenov Electrical Engineering, Mathematics and Computer Science

Contributors

M.M. de Weerdt Algorithmics - EEMCS (mentor)
I.K. Hanou Algorithmics - EEMCS (mentor)
J. Sun Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Published Date

27-06-2024

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Solutions for the Train Unit Shunting Problem are constantly being researched and improved to be- come more efficient and match the needs of train transport in the Netherlands. For this reason, we are exploring new ways to find patterns in the train data to identify where those solutions could be en- hanced. More specifically, we are trying to find patterns that make identifying different locations possible. We identify patterns in the capacity of the shunting yards and the types of trains used in various locations, which result in reasonable ac- curacy in classification. Some locations operate closer to their capacity, and some require longer paths to get inside the shunting yards. These find- ings could be helpful not only for the planning algo- rithms but also in identifying which locations might need expansion or restructuring and where more of the train fleet should be allocated.

Files

Final_Paper.pdf
(.pdf | 0.32 Mb)