Abstract
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.
Highlights
In recent years, there has been a global increase in the demand for rail transport, and rail usage is expected to continue to increase worldwide [1]
We propose a method to advance dynamic risk management in railway stations
To execute the adaptive neuro-fuzzy inference system (ANFIS) model to manage risk factors in the station, we evaluate the performance of the suggested system for overcrowding
Summary
There has been a global increase in the demand for rail transport, and rail usage is expected to continue to increase worldwide [1]. Passenger streams in railway systems are growing dramatically in many cities over the world with the rapid development of rail transit. This reflects on stations that face enormous pressure from passenger congestion and the high level of overcrowding in peak times. The metro systems in Beijing and Shanghai provide a daily transport service for more than nine million passengers, and statistics indicate that the annual usage has almost doubled from 2011 to 2015 [2,3].
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