The language of driving

Anthony McDonald, John Lee, Nazan Aksan, Jeffrey Dawson, Jon Tippin, Matthew Rizzo

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Recent advances in onboard vehicle data recording devices have created an abundance of naturalistic driving data. The amount of data exceeds the resources available for analysis; this situation forces researchers to focus on analyses of critical events and to use simple heuristics to identify those events. Critical event analysis eliminates the context that can be critical in understanding driver behavior and can reduce the generalizability of the analysis. This work introduced a method of naturalistic driving data analysis that would allow researchers to examine entire data sets by reducing the sets by more than 90%. The method utilized a symbolic data reduction algorithm, symbolic aggregate approximation (SAX), which reduced time series data to a string of letters. SAX can be applied to any continuous measurement, and SAX output can be reintegrated into a data set to preserve categorical information. This work explored the application of SAX to speed and acceleration data from a naturalistic driving data set and demonstrated SAX's integration with other methods that could begin to tame the complexity of naturalistic data.

Original languageEnglish (US)
Pages (from-to)22-30
Number of pages9
JournalTransportation Research Record
Issue number2392
StatePublished - Dec 1 2013
Externally publishedYes

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'The language of driving'. Together they form a unique fingerprint.

Cite this