Sensor Based Smart Railway Accident Detection and Prevention System for Smart Cities Using Real Time Mobile Communication

Mustafa, Ali; Rasheed, Ozain; Shahzad, Rehman; Ullah, Farman; Ahmed, Salman

doi:10.1007/s11277-022-09992-5

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…There are many railway accident prevention strategies including the use of reinforcement learning method proposed by [12], installation of real time mobile communication tools [13], use of sensors to detect movement on rails and to provide alert messages, classification of the cause of railway accident using the entropy weight method leading to railway accident evaluation standard [14]. Habib [15] suggested the use of artificial intelligence and machine learning techniques to identify risk of accidents from "Experience Feedback".…”

Section: Related Workmentioning

confidence: 99%

Nature-Inspired Optimization: Optimizing Distance of Emergency Response Wagons en Route to Railway Crossing Accident Location

Agbehadji,

Millham,

Fong

et al. 2024

Frontiers in Artificial Intelligence and Applications

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The problem is that when an accident occurs at a railway crossing, emergency response wagons are unable to get to the accident location on time. In this study, we consider the location of emergency response wagons and railway accident location and apply a nature-inspired search strategy as a solution to compute the optimal distance for an emergency response wagon such that when an accident occurs the expected time to reach the accident location can be minimized. The outcome suggests the ideal objective function, haversine method, because it produced the optimal minimal distance of 994,691.90km with a computational time of 1.05s for KSA over comparative algorithms namely BAT and (Ant Colony Optimization) ACO.

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Section: Related Workmentioning

confidence: 99%

Nature-Inspired Optimization: Optimizing Distance of Emergency Response Wagons en Route to Railway Crossing Accident Location

Agbehadji,

Millham,

Fong

et al. 2024

Frontiers in Artificial Intelligence and Applications

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show abstract

“…Pakistan railway freight also transported 7.4 million tonnes in the year 2020 [ 6 ]. However, in the past few years, several serious accidents took place that caused huge human and economic losses [ 7 ]. Such accidents can happen due to human error, weather conditions, or faulty railway tracks.…”

Section: Introductionmentioning

confidence: 99%

Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data

Rustam

Ishaq

Hashmi

et al. 2023

Sensors

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Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings.

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A migration strategy based on cluster collaboration predictions for mobile edge computing-enabled smart rail system

Cao,

Yu,

Yang

2024

J Supercomput

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Sensor Based Smart Railway Accident Detection and Prevention System for Smart Cities Using Real Time Mobile Communication

Cited by 6 publications

References 33 publications

Nature-Inspired Optimization: Optimizing Distance of Emergency Response Wagons en Route to Railway Crossing Accident Location

Nature-Inspired Optimization: Optimizing Distance of Emergency Response Wagons en Route to Railway Crossing Accident Location

Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data

A migration strategy based on cluster collaboration predictions for mobile edge computing-enabled smart rail system

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