IMPROVE MACHINE LEARNING FOR HYBRID TRAFFIC IN RAILWAY STATION USING ARTIFICIAL INTELLIGENCE

Abstract

 Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology such as machine learning (ML) to analyses accidents and enhance safety systems. We propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. Machine learning and other forms of AI have made rapid progress over recent years. To showcase the potential insights gained by these methods, we apply them to delay log data from the GO Rail network in the Greater Golden Horseshoe area of Ontario, Canada. Recently, growing attention has been given to the application of Natural Language Processing (NLP) to aid the practice of analyzing a large corpus of textual data, but only limited studies to date in railway safety use such techniques and none address railway accident causation analysis. The most popular and efficient methods used in all transport modes are ANN, SVM, Hidden Markov Models and Bayesian models. The type of the analytical technique is mainly driven by the purpose of the safety analysis performed. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes.