# Short Ticketing Detection Framework Analysis Report

arXiv: https://arxiv.org/abs/2510.23619 · HTML: https://mlsanity.com/paper/2510.23619/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 5 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Abstract (arXiv)

This report presents a comprehensive analysis of an unsupervised multi-expert machine learning framework for detecting short ticketing fraud in railway systems. The study introduces an A/B/C/D station classification system that successfully identifies suspicious patterns across 30 high-risk stations. The framework employs four complementary algorithms: Isolation Forest, Local Outlier Factor, One-Class SVM, and Mahalanobis Distance. Key findings include the identification of five distinct short ticketing patterns and potential for short ticketing recovery in transportation systems.