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.

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 utilising the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry.

Railway industry plays a critical role in transportation and transit systems attributed to the ever-growing demand for catering to both freight and passengers. However, owing to many challenges faced by railway stations such as harsh environments, traffic flow, safety and security risks, new and adaptive systems employing new technology are recommended. In this review, several wireless sensor networks (WSNs) applications are proposed for use in railway station systems, including advanced WSNs, which will enhance security, safety, and decision-making processes to achieve more cost-effective management in railway stations, as well as the development of integrated systems. The size, efficiency, and cost of WSNs are influential factors that attract the railway industry to adopt these devices. This paper presents a review of WSNs that have been designed for uses in monitoring and securing railway stations. This article will first briefly focus on the presence of different WSN applications in diverse applications. In addition, it is important to note that exploitation of the state-of-the-art tools and techniques such as WSNs to gain an enormous amount of data from a railway station is a new and novel concept requiring the development of artificial intelligence methods, such machine learning, which will be vital for the future of the railway industry.

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems.

For both passenger and freight transportation, railroad operations must be dependable, accessible, maintained, and safe (RAMS). In many urban areas, railway stations risk and safety accidents represent an essential safety concern for daily operations. Moreover, the accidents lead to damage to market reputation, including injuries and anxiety among the people and costs. This stations under pressure caused by higher demand which consuming infrastructure and raised the safety administration consideration. To analysing these accidents and utilising the technology such AI methods to enhance safety, it is suggested to use unsupervised topic modelling for better understand the contributors to these extreme accidents. It is conducted to optimise Latent Dirichlet Allocation (LDA) for fatality accidents in the railway stations from textual data gathered RSSB including 1000 accidents in the UK railway station. This research describes using the machine learning topic method for systematic spot accident characteristics to enhance safety and risk management in the stations and provides advanced analysing. The study evaluates the efficacy of text by mining from accident history, gaining information, lesson learned and deeply coherent of the risk caused by assessing fatalities accidents for large and enduring scale. This Intelligent Text Analysis presents predictive accuracy for valuable accident information such as root causes and the hot spots in the railway stations. Further, the big data analytics ’ improvement results in an understanding of the accidents’ nature in ways not possible if a considerable amount of safety history and not through narrow domain analysis of the accident reports. This technology renders stand with high accuracy and a beneficial and extensive new era of AI applications in railway industry safety and other fields for safety applications.

This paper highlights a risk-based decision-making framework on a basis of probabilistic risk assessment (PRA). Its aim is to enable stakeholders of transport infrastructures to systematically and effectively allocate their limited resources and consequently improve resilience when facing the potential risk of a terrorist attack. The potential risk of a terrorist attack affects the inter-operation of transportation infrastructures including airports and rail stations, the regional economy, and imposes additional costs of security or any countermeasures. This novel framework is thus established in order to model the security system, to consider a multitude of threat scenarios, and to assess the decisions and choices taken by the aggressors during various stages of their attack. The framework has capability to identify the state of partial neutralization, which reveals the losses incurred when the terrorist could not reach the primary target. In this study, an underground railway station interconnected to an international airport has been used as a case study to demonstrate the effectiveness of this novel framework. By the rigorous assessment of potential losses during a variety of threat scenarios, four countermeasures that could minimise losses are proposed: screening of passengers by observation techniques (SPOT), a surveillance system, increase of the cargo screening rate, and blast-resistant cargo containers. The cost and efficiency assessment is employed to determine the most suitable countermeasures when the value of the security measures equal their cost. Note that ongoing research is still needed to establish better countermeasures since there is no end to the creativity of terrorists. The new technology, such as wireless sensors, will play an important role in the security system in the future. In particular, this study will help insurance and rail industries to model and manage risk profiles at critical infrastructure.