Developments in text mining now allow useful information to be automatically extracted from text. The Federal Railroad Administration (FRA) publishes a database of railroad equipment accidents. These accident records contain numeric data describing the accident and a text description of the accident. This paper will discuss how latent Dirichlet analysis (LDA), a text-mining algorithm, can be used to identify major recurring accident topics from the text in the FRA reports. Equipment accident reports from 2005 to 2015 were studied. This analysis identified railroad grade crossing accidents with large trucks, shoving accidents, and hump yard accidents as major topics in the accident reports. An alternative method of analyzing the text, text clustering, was also used to study the FRA data. Visualizations of the text also provide useful information about the major types of railroad accidents.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Identifying Themes in Railroad Equipment Accidents Using Text Mining and Text Visualization


    Beteiligte:

    Kongress:

    International Conference on Transportation and Development 2016 ; 2016 ; Houston, Texas



    Erscheinungsdatum :

    20.06.2016




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Text Mining the Contributors to Rail Accidents

    Brown, Donald E | Online Contents | 2016



    Text Mining Analysis of Railroad Accident Investigation Reports

    Williams, Trefor / Betak, John / Findley, Bridgette | British Library Conference Proceedings | 2016


    Discovering latent themes in aviation safety reports using text mining and network analytics

    Xing, Yingying / Wu, Yutong / Zhang, Shiwen et al. | Elsevier | 2024

    Freier Zugriff

    Railroad accidents

    Engineering Index Backfile | 1894