UNSUPERVISED MACHINE LEARNING FOR MANAGING SAFETY ACCIDENTS IN RAILWAY STATIONS

Abstract

For both passenger and freight transportation, railroad operations must be dependable, accessible, maintained, and
safe (RAMS). In many urban areas, railway stationsrisk and safety accidentsrepresent 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.