Hybrid Railway Safety Optimization With Neural Networks and Random Forests

Abstract

Enhancing safety protocols in train stations requires proactive approaches to foresee and reduce any mishaps. In order to maximize safety in railroad environments, this study presents a novel machine learning framework that combines neural networks with random forest techniques. This hybrid model departs from traditional single-algorithm approaches by utilizing a variety of methods to enhance accident investigation and prediction. The chapter focuses on using this hybridized method, showcasing the functions of random forests and neural networks in predicting future events and assessing accident data from the past. The strategy of incorporating neural networks (multi-layer perceptron (MLP)) and random forests into the accident analysis framework is covered in the study. It also investigates if this model can be implemented in real-time and at scale within railway station safety systems. The results of the research reveal that the hybridized machine learning approach is a promising tool for improving safety standards in railroad stations.