Applying topic modeling to railroad grade crossing accident report text

Trefor Williams, Christie Nelson, John Betak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The FRA railroad grade crossing accident database contains text comment fields that may provide additional information about grade crossing accidents. New text mining algorithms provide the potential to automatically extract information from text that can enhance traditional numeric analyses. Topic modeling algorithms are statistical methods that analyze the words of original texts to automatically discover the themes that run through them. A frequently used topic-modeling algorithm is Latent Dirichlet Analysis (LDA). In this paper we will show several examples of how labeled LDA can be applied to the FRA grade crossing data to better understand categories of words and phrases that are associated with various types of grade crossing accidents.

Original languageEnglish (US)
Title of host publication2015 Joint Rail Conference, JRC 2015
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791856451
DOIs
StatePublished - 2015
EventASME/ASCE/IEEE 2015 Joint Rail Conference, JRC 2015 - San Jose, United States
Duration: Mar 23 2015Mar 26 2015

Publication series

Name2015 Joint Rail Conference, JRC 2015

Other

OtherASME/ASCE/IEEE 2015 Joint Rail Conference, JRC 2015
Country/TerritoryUnited States
CitySan Jose
Period3/23/153/26/15

All Science Journal Classification (ASJC) codes

  • Transportation
  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'Applying topic modeling to railroad grade crossing accident report text'. Together they form a unique fingerprint.

Cite this