Abstract
Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, 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. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. 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. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.
Highlights
Railway station environments are dynamic, and this dynamicity varies according to size and location
Of the many applications that have been applied to convolutional neural network (CNN), in this subsection, we present those that are related to railways
We explore deep learning (DL) by utilising a convolutional neural network (CNN) to detect passenger falls
Summary
Railway station environments are dynamic, and this dynamicity varies according to size and location. The FSTs in crowded situations can have serious consequences; historically, many people have died or suffered serious injury during events such as religious pilgrimages Such risks increase when the railway industry’s growth level is inadequate to serve the market demand for train travel. In railway stations, detecting such risks relies on CCTV or staff observations; this approach has the potential for human error and may not result in a timely response, which can exacerbate the consequences. The data used for monitoring can be collected at fixed points or be installed on moving trains or other vehicles, such as drones [27], [28] These datagathering systems and their configuration can integrate with the Internet of Things (IoT), which is a framework suitable for big data technology, smart stations, smart cities and smart maintenance [29]–[33].
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