A Deep Neural Network Approach to Fare Evasion

TL;DR

This paper presents a novel approach using deep learning, specifically LSTM models trained on keypoint data from video footage, to predict fare evasion in public transport, aiming to improve detection and reduce financial losses.

Contribution

It introduces a new method combining keypoint extraction and LSTM models for real-time fare evasion prediction, enhancing accuracy over traditional inspection methods.

Findings

Promising real-time prediction accuracy of passenger actions.

Potential for integrating ReID models for improved identification.

Method can help target areas with high fare evasion rates.

Abstract

Fare evasion is a problem for public transport companies, with LSTM models this issue can help companies get an analytical insight into where this issue occurs the most, to prevent capital loss. In addition to the financial burden this problem causes, having more inspectors is not enough to alleviate the problem. The purpose of this study is to find a different way to predict fare evasion in the public transport sector. Through the use of keypoint extractions of passengers in video footage, an LSTM model is trained on those keypoints to help predict the actions of passengers between payments and evasions. The results were promising when it came to predicting the actions of passengers on real-time footage. Thus a sophisticated approach can help to decrease the fare evasion problem. A ReID model can be used alongside the LSTM model for better accuracy, as there is always the chance that a person might only pay for the fare at a later stage. With both models, it is possible for public transport companies to start narrowing down where the root of their fare evasion problems emerges.