  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.
