Variations on a theme: Topic modeling of naturalistic driving data

Elease McLaurin, Anthony D. McDonald, John D. Lee, Nazan Aksan, Jeffrey Dawson, Jon Tippin, Matthew Rizzo

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

6 Scopus citations

Abstract

This paper introduces Probabilistic Topic Modeling (PTM) as a promising approach to naturalistic driving data analyses. Naturalistic driving data present an unprecedented opportunity to understand driver behavior. Novel strategies are needed to achieve a more complete picture of these datasets than is provided by the local event-based analytic strategy that currently dominates the field. PTM is a text analysis method for uncovering word-based themes across documents. In this application, documents were represented by drives and words were created from speed and acceleration data using Symbolic Aggregate approximation (SAX). A twenty-topic Latent Dirichlet Allocation (LDA) topic model was developed using words from 10,705 documents (real-world drives) by 26 drivers. The resulting LDA model clustered the drives into meaningful topics. Topic membership probabilities were successfully used as features in subsequent analyses to differentiate between healthy drivers and those suffering from Obstructive Sleep Apnea.

Original languageEnglish (US)
Title of host publication2014 International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2014
PublisherHuman Factors an Ergonomics Society Inc.
Pages2107-2111
Number of pages5
ISBN (Electronic)9780945289456
DOIs
StatePublished - 2014
Event58th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2014 - Chicago, United States
Duration: Oct 27 2014Oct 31 2014

Publication series

NameProceedings of the Human Factors and Ergonomics Society
Volume2014-January
ISSN (Print)1071-1813

Other

Other58th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2014
CountryUnited States
CityChicago
Period10/27/1410/31/14

ASJC Scopus subject areas

  • Human Factors and Ergonomics

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  • Cite this

    McLaurin, E., McDonald, A. D., Lee, J. D., Aksan, N., Dawson, J., Tippin, J., & Rizzo, M. (2014). Variations on a theme: Topic modeling of naturalistic driving data. In 2014 International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2014 (pp. 2107-2111). (Proceedings of the Human Factors and Ergonomics Society; Vol. 2014-January). Human Factors an Ergonomics Society Inc.. https://doi.org/10.1177/1541931214581443