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

Alawad, H ORCID: https://orcid.org/0000-0001-9871-1588, An, M ORCID: https://orcid.org/0000-0002-1069-7492 and Kaewunruen, S ORCID: https://orcid.org/0000-0003-2153-3538 2020, 'Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations' , Applied Sciences, 10 (15) , e5156.

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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.

Item Type: Article
Additional Information: ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 2076-3417 **History: published 27-07-2020; accepted 23-07-2020
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Applied Sciences
Publisher: MDPI
ISSN: 2076-3417
Related URLs:
Funders: Australian Academy of Science and the Japan Society for the Promotion of Sciences, European Commission H2020-RISE
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 29 Jul 2020 10:50
Last Modified: 16 Feb 2022 05:12
URI: https://usir.salford.ac.uk/id/eprint/57735

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