Learning from accidents : machine learning for safety at railway stations

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 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

Risk management prediction for overcrowding in railway stations utilising Adaptive Nero Fuzzy Inference System (ANFIS)

In this research, an intelligent system for managing risks is developed with a framework to aid in managing the risks in the railway stations. A method to advance risk management in the railway stations is needed in order to minimize risk through an automated process taking into consideration all the factors in the system and how they work together to provide an acceptable level of safety and security. Thus, the Adaptive Nero Fuzzy Inference System (ANFIS) is proposed to improve risk management as an intelligently selected model which is powerful in dealing with uncertainties in risk variables. The methods of artificial neural network (ANN) and Fuzzy interface system (FIS) have been proven as tools for measuring risks in many fields. In this case study, the railway is selected as a place for managing the risks of overcrowding in the railway stations taking two parameters as input for risk value output using a hybrid model, which has the potency to deal with risk uncertainties and to learn by ANN training processes. The results show that the ANFIS method is more promising in the management of station risks. The framework can be applied for other risks in the station and more for a wide range of other systems. Also, ANFIS has the ability to learn from past risk records for future prediction. Clearly, the risk indexes are essential to reflect the actual condition of the station and they can indicate a high level of risks at the early stage, such as with overcrowding. The dynamic model of risk management can define risk levels and aid the decision makers by convenient and reliable results based on recorded data. Finally, the model can be generalised for other risks

A deep learning approach towards railway safety risk assessment

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

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

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

Indicators of Asthma Control in Asthmatic Patients: Are they related to Depression?

BACKGROUND: Many patients with chronic illnesses suffer from depression. A poorly controlled asthma that presents with repeated attacks of breathlessness is a recognised risk factor for bipolar disorders. On the other hand, depression can aggravate asthma symptoms.AIM: The aim of this study is to determine the indicators of the asthma control test (ACT) among the asthmatic patients who attend the outpatient clinic for follow-up and to investigate the relation between these indicators and clinical depression, using a standardised depression scale.METHODS: A total of 38 adult asthmatic patients (29 women, age 17–85 years), with variable levels of control, were assessed using the ACT and the Beck Depression Inventory (BDI). Data obtained were analysed with the Statistical Package for the Social Sciences (SPSS Inc., Chicago, IL, USA) version 20. The association of depression with the various indicators of asthma control was analysed with the Chi-Square test. Statistical significance was determined at p< 0.05.RESULTS: The uncontrolled asthma is associated with depression in 37% of all participants compared to 0% in the well-controlled group (p = 0.002). Depression is significantly related to asthma interference with daily activities (p = 0.003), breathlessness (p < 0.001), night symptoms (p = 0.036), use of bronchodilators (p = 0.007), and poor compliance with medical treatment (p = 0.003). The poor educational attainment and comorbidities have significant relations to both uncontrolled asthma and clinical depression (p < 0.05).CONCLUSION: All indicators of the poor asthma control are associated with clinical depression. A significant proportion of the uncontrolled asthma patients suffers from depression. The possibility of improving asthma control with a pharmacological treatment of depression has to be considered

Wireless Sensor Networks::Toward Smarter Railway Stations

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