Risk Assessment Model for Railway Passengers on a Crowded Platform

Terabe, Shintaro; Kitasaka, Takayuki; Yaginuma, Hideki; Kang, Nan; Tanaka, Kosuke

doi:10.1177/0361198118821925

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…If the railway sections approaching the trains are equipped with a semi-autoblocking system, in this case, the system that transmits the message about the approaching trains to the creatures, the noise generator is activated at the contact of the track relay of the rail chain or track sensors at the same distance from the point where the creatures gather in the even and odd directions [15][16][17][18][19][20][21][22][23][24].…”

Section: Algorithm Of Operation Of the System Of Transmission Of A Wa...mentioning

confidence: 99%

System of automated warning messages to creatures moving on railway tracks about the approach of rolling stock

Boltayev,

Kosimova,

Astanaliev

et al. 2023

E3S Web Conf.

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Incidents of train collisions with creatures on railway tracks are one of the major problems for railway transport today. The scientific article presents the results of the analysis of cases of trains hitting people and creatures on railway sections. Taking this into account, the aim of the scientific work is to develop a system of automated warning messages to creatures on railway tracks of approaching trains. In the creation of the system, the notification of the approaching trains to the creatures was selected based on the frequency of hearing of the creatures that gather a lot on the railway sections. In the article, the working algorithm of the system for notifying creatures of the noise generator of the approach of trains, as well as the types of auto-blocking systems on railway sections, to start the noise generator that transmits the message of the approach of trains, if the railway tracks are equipped with the auto-blocking system, the rail chains or the railway tracks are equipped with a semi auto-blocking system connecting the road sensors that notify when the train is approaching to the contacts of the road relays, and recommendations on the selection of frequencies for the noise generator of the train approach warning system based on the hearing frequency of the creatures that gather in large numbers around the railway sections.

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Section: Algorithm Of Operation Of the System Of Transmission Of A Wa...mentioning

confidence: 99%

System of automated warning messages to creatures moving on railway tracks about the approach of rolling stock

Boltayev,

Kosimova,

Astanaliev

et al. 2023

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…For example, In Japan in 2016, 420 accidents occurred that included being struck by a train, which resulted in 202 deaths. This including of those 420 accidents, 179 (resulting in 24 fatalities) included falling from a platform and following injury or death as a consequence of hitting with a train [1]. In the UK, 2019/20, it has been reported that Most passenger injuries occur from accidents in stations.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Machine Learning for Managing Safety Accidents in Railway Stations

Alawad

Kaewunruen

2023

IEEE Access

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For both passenger and freight transportation, railroad operations must be dependable, accessible, maintained, and safe (RAMS). In many urban areas, railway stations risk and safety accidents represent an essential safety concern for daily operations. Moreover, the accidents lead to damage to market reputation, including injuries and anxiety among the people and costs. This stations under pressure caused by higher demand which consuming infrastructure and raised the safety administration consideration. To analysing these accidents and utilising the technology such AI methods to enhance safety, it is suggested to use unsupervised topic modelling for better understand the contributors to these extreme accidents. It is conducted to optimise Latent Dirichlet Allocation (LDA) for fatality accidents in the railway stations from textual data gathered RSSB including 1000 accidents in the UK railway station. This research describes using the machine learning topic method for systematic spot accident characteristics to enhance safety and risk management in the stations and provides advanced analysing. The study evaluates the efficacy of text by mining from accident history, gaining information, lesson learned and deeply coherent of the risk caused by assessing fatalities accidents for large and enduring scale. This Intelligent Text Analysis presents predictive accuracy for valuable accident information such as root causes and the hot spots in the railway stations. Further, the big data analytics ' improvement results in an understanding of the accidents' nature in ways not possible if a considerable amount of safety history and not through narrow domain analysis of the accident reports. This technology renders stand with high accuracy and a beneficial and extensive new era of AI applications in railway industry safety and other fields for safety applications.

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