Development of Segmentation Method for Freight Work Level Using Machine Learning Models
( Pp. 13-23)
More about authors
Ignatov Yuriy Yu.
Cand. Sci. (Sociol.), Associate Professor; associate professor, Academy “Higher Engineering School”
Russian University of Transport (MIIT)
Moscow, Russian Federation
Russian University of Transport (MIIT)
Moscow, Russian Federation
Abstract:
This study explores methods for automatically segmenting railway stations using machine learning models by analyzing cargo handling patterns. Automated segmentation techniques help identify key performance indicators and decision-making patterns useful for effective management strategies. Data preprocessing and clustering algorithms are employed with optimized machine learning models. Neural network architectures and deep learning methodologies are developed to classify stations dynamically during operational load monitoring. Practical recommendations are given for designing modular systems capable of intelligently distributing stations, clients, and other entities across targeted segments while accounting for data non-linearities and specific characteristics.
How to Cite:
Ignatov Yu.Yu. Development of segmentation method for freight work level using machine learning models. Computational Nanotechnology. 13, 1 (2026), 13–23. DOI: 10.33693/2313-223X-2026-13-1-13-23. EDN: LXNFFT
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Keywords:
dataset, clustering, machine learning, model, neural network, normalization, layer, station.
