The language of driving

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

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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
DOIs
StatePublished - Dec 1 2013

Fingerprint

Data recording
Time series
Data reduction

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

Cite this

McDonald, A., Lee, J., Aksan, N., Dawson, J., Tippin, J., & Rizzo, M. (2013). The language of driving. Transportation Research Record, (2392), 22-30. https://doi.org/10.3141/2392-03

The language of driving. / McDonald, Anthony; Lee, John; Aksan, Nazan; Dawson, Jeffrey; Tippin, Jon; Rizzo, Matthew.

In: Transportation Research Record, No. 2392, 01.12.2013, p. 22-30.

Research output: Contribution to journalArticle

McDonald, A, Lee, J, Aksan, N, Dawson, J, Tippin, J & Rizzo, M 2013, 'The language of driving', Transportation Research Record, no. 2392, pp. 22-30. https://doi.org/10.3141/2392-03
McDonald A, Lee J, Aksan N, Dawson J, Tippin J, Rizzo M. The language of driving. Transportation Research Record. 2013 Dec 1;(2392):22-30. https://doi.org/10.3141/2392-03
McDonald, Anthony ; Lee, John ; Aksan, Nazan ; Dawson, Jeffrey ; Tippin, Jon ; Rizzo, Matthew. / The language of driving. In: Transportation Research Record. 2013 ; No. 2392. pp. 22-30.
@article{0cdad16820324f8ea5d80a9c374ad431,
title = "The language of driving",
abstract = "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.",
author = "Anthony McDonald and John Lee and Nazan Aksan and Jeffrey Dawson and Jon Tippin and Matthew Rizzo",
year = "2013",
month = "12",
day = "1",
doi = "10.3141/2392-03",
language = "English (US)",
pages = "22--30",
journal = "Transportation Research Record",
issn = "0361-1981",
publisher = "US National Research Council",
number = "2392",

}

TY - JOUR

T1 - The language of driving

AU - McDonald, Anthony

AU - Lee, John

AU - Aksan, Nazan

AU - Dawson, Jeffrey

AU - Tippin, Jon

AU - Rizzo, Matthew

PY - 2013/12/1

Y1 - 2013/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84897128725&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84897128725&partnerID=8YFLogxK

U2 - 10.3141/2392-03

DO - 10.3141/2392-03

M3 - Article

C2 - 26203202

AN - SCOPUS:84897128725

SP - 22

EP - 30

JO - Transportation Research Record

JF - Transportation Research Record

SN - 0361-1981

IS - 2392

ER -