Wireless Sensor Networks: Toward Smarter Railway Stations

Due to the highly developed rail transit over the past decades, the phenomena of complex individual self-organized behaviors and mass crowd dynamics have become a great concern in the train station. In order to understand passengers’ walking-edge effect and analyze the relationship between the layout and sustainable service abilities of the train station, a heuristics-based social force model is proposed to elaborate the crowd dynamics. Several evacuation scenarios are implemented to describe the walking-edge effect in a train station with the evacuation efficiency, pedestrian flow, and crowd density map. The results show that decentralizing crowd flow can significantly increase the evacuation efficiency in different scenarios. When the exits are far away from the central axis of the railway station, the walking-edge effect has little influence on the evacuation efficiency. Obstacles can guide the movement of passengers by channelizing pedestrian flows. In addition, a wider side exit of the funnel-shaped corridors can promote walking-edge effect and decrease the pressure among a congested crowd. Besides providing a modified social force model with considering walking-edge effect, several suggestions are put forward for managers and architects of the train station in designing sustainable layouts.

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“…f iW and other parameters are shown in Equation (1). The illustration of the social force of pedestrian i is depicted in Figure 1a.…”

Section: Evacuation Modelmentioning

confidence: 99%

“…Over the past decade, traveling demands have increased rapidly due to world population growth [1]. As an effective mode of transport, especially with the massive construction of high-speed rail, train systems have become very popular and are now the mainstay of transportation in the metropolis [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Walking-Edge Effect in Train Station Evacuation Scenarios: A Sustainable Transportation Perspective

et al. 2019

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“…Moreover, insofar as SPOT does not reduce risk much, it is recommended that a cost-benefit analysis of the SPOT program be implemented, and a comparison of the SPOT with other security methods [43] could potentially achieve a less expensive mix of security measures such as the installation of an elaborate video surveillance system (C2) and the installation of blast-resistant cargo containers (C4). We believe that quick technological improvements will aid the security systems, making use of effective and low-cost countermeasures such as smart and wireless sensors [55].…”

Section: Cost and Efficiency Assessment Of Countermeasuresmentioning

confidence: 99%

A Decision Framework for Managing the Risk of Terrorist Threats at Rail Stations Interconnected with Airports

Kaewunruen

Alawad

Cotruta

2018

Safety

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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.

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“…The data used for monitoring can be collected at fixed points or be installed on moving trains or other vehicles, such as drones [27], [28]. Moreover, these data-gathering systems and their configuration can integrate with the Internet of Things (IoT), which is a framework suitable for big data technology, smart stations, smart cities and smart maintenance [29]- [33].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

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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.

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Wireless Sensor Networks: Toward Smarter Railway Stations

Cited by 42 publications

References 59 publications

Analysis of Walking-Edge Effect in Train Station Evacuation Scenarios: A Sustainable Transportation Perspective

Analysis of Walking-Edge Effect in Train Station Evacuation Scenarios: A Sustainable Transportation Perspective

A Decision Framework for Managing the Risk of Terrorist Threats at Rail Stations Interconnected with Airports

A Deep Learning Approach Towards Railway Safety Risk Assessment

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