Unsupervised machine learning for managing safety accidents in railway stations

نویسندگان

چکیده

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

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