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Article

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations

1
Birmingham Centre for Railway Research and Education, The University of Birmingham, Birmingham B15 2TT, UK
2
The School of Science, Engineering and Environment, The University of Salford, Manchester M5 4WT, UK
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(15), 5156; https://doi.org/10.3390/app10155156
Received: 21 June 2020 / Revised: 19 July 2020 / Accepted: 23 July 2020 / Published: 27 July 2020
(This article belongs to the Special Issue Extreme Sciences and Engineering Ⅱ)
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. View Full-Text
Keywords: risk management; railway station; overcrowding risk; fuzzy interface systems; neural network; artificial intelligence risk management; railway station; overcrowding risk; fuzzy interface systems; neural network; artificial intelligence
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MDPI and ACS Style

Alawad, H.; An, M.; Kaewunruen, S. Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations. Appl. Sci. 2020, 10, 5156. https://doi.org/10.3390/app10155156

AMA Style

Alawad H, An M, Kaewunruen S. Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations. Applied Sciences. 2020; 10(15):5156. https://doi.org/10.3390/app10155156

Chicago/Turabian Style

Alawad, Hamad, Min An, and Sakdirat Kaewunruen. 2020. "Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations" Applied Sciences 10, no. 15: 5156. https://doi.org/10.3390/app10155156

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