[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127561-en":3,"doc-seo-127561-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127561,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Unsupervised machine learning for managing safety accidents in railway stations","Research addresses how railway stations manage safety accidents under growing passenger and freight demands, where injuries, anxiety, and reputational damage increase alongside rising safety administration needs. Using unsupervised topic modelling, the study optimizes Latent Dirichlet Allocation (LDA) on textual accident histories from RSSB, covering 1,000 UK station accidents. The resulting intelligent text analysis supports systematic spot-characterization, improves understanding of fatality drivers, and helps identify root causes and hotspots for stronger risk management.","University of Birmingham  \nUnsupervised machine learning for managing safety accidents in railway stations  \nAlawad, Hamad Ali H; Kaewunruen, Sakdirat  \nDOI:  \n10.1109/ACCESS.2023.3264763  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nAlawad, HAH & Kaewunruen, S 2023, 'Unsupervised machine learning for managing safety accidents in railway stations', IEEE Access. [https://doi.org/10.1109/ACCESS.2023.3264763](https://doi.org/10.1109/ACCESS.2023.3264763)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 05. Aug. 2026  \nThis article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/ACCESS.2023.3264763  \nAlawad and Kaewunruen :Unsupervised machine learning for Safety accidents in the railway stations  \nDate of publication xxxx 00, 0000, date of current version xxxx 00, 0000.  \nDigital Object Identifier 10. 1109/ACCESS.2017.DOI  \nUnsupervised machine learning for managing safety accidents in railway stations  \nHAMAD ALAWAD1, SAKDIRAT KAEWUNRUEN 1,*,  \n1 1The Birmingham Centre for Railway Research and Education, University of Birmingham, Birmingham, B15 2TT, Birmingham, UK (*Corresponding author, e-mails: [s.kaewunruen@bham.ac.uk](s.kaewunruen@bham.ac.uk))  \nABSTRACT 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 pro","cbCairvnBdTJCkRL","https://ap.wps.com/l/cbCairvnBdTJCkRL","pdf",1144075,1,16,"English","en",105,"# Introduction\n## RAMS and railway station safety context\n# Methodology\n## Unsupervised topic modelling approach\n## Latent Dirichlet Allocation (LDA) optimization\n# Data and Experimental Setup\n## RSSB textual accident dataset (UK stations)\n# Results and Discussion\n## Predictive accuracy, root causes, and hotspots\n## Benefits of big data analytics vs narrow analysis\n# Conclusion","[{\"question\":\"What problem does the research target in railway stations?\",\"answer\":\"It targets safety accidents in railway stations and the need to understand contributors to fatality events to improve risk management under increasing demand and congestion.\"},{\"question\":\"Which unsupervised technique is used to analyze accident texts?\",\"answer\":\"The study uses unsupervised topic modelling with Latent Dirichlet Allocation (LDA) optimized for fatality accidents based on textual data.\"},{\"question\":\"What outputs are aimed for from the accident-history mining?\",\"answer\":\"The approach is designed to provide predictive information such as root causes and safety hotspots, improving systematic characterization of accident characteristics.\"}]","Unsupervised machine learning for managing safety accidents in railway stations | 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