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Hardware and software for collecting microscopic trajectory data on naturalistic driving behavior.

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Title: Hardware and software for collecting microscopic trajectory data on naturalistic driving behavior.
Authors: Shrestha, Deepak1, Lovell, David J.2, Tripodis, Yorghos3
Source: Journal of Intelligent Transportation Systems. 2017, Vol. 21 Issue 3, p202-213. 12p.
Subjects: Traffic flow, Traffic engineering software, Automobile driving, Computer software
Abstract: This article presents a method to collect naturalistic microscopic longitudinal vehicle trajectory data with a modest budget. The drivers studied are not aware that they are participating in an experiment; hence one can collect naturalistic driving behavior. This article presents the hardware and software developed, and we include a detailed example of a particular case study that was conducted with data collected from the system. The case study examines drivers' willingness to accept very short headways, and casts that behavior in light of their subsequent lane-changing decisions. The data show a statistically defensible connection between these behaviors. These phenomena are not new, but highlight the importance of the data quality and of observing naturalistic driving behavior, and this article demonstrates a method to calibrate specific parameters related to the behavior. [ABSTRACT FROM PUBLISHER]
Copyright of Journal of Intelligent Transportation Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 123150358
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
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  Data: Hardware and software for collecting microscopic trajectory data on naturalistic driving behavior.
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  Data: <searchLink fieldCode="AR" term="%22Shrestha%2C+Deepak%22">Shrestha, Deepak</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lovell%2C+David+J%2E%22">Lovell, David J.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Tripodis%2C+Yorghos%22">Tripodis, Yorghos</searchLink><relatesTo>3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Transportation+Systems%22">Journal of Intelligent Transportation Systems</searchLink>. 2017, Vol. 21 Issue 3, p202-213. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Traffic+flow%22">Traffic flow</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+engineering+software%22">Traffic engineering software</searchLink><br /><searchLink fieldCode="DE" term="%22Automobile+driving%22">Automobile driving</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This article presents a method to collect naturalistic microscopic longitudinal vehicle trajectory data with a modest budget. The drivers studied are not aware that they are participating in an experiment; hence one can collect naturalistic driving behavior. This article presents the hardware and software developed, and we include a detailed example of a particular case study that was conducted with data collected from the system. The case study examines drivers' willingness to accept very short headways, and casts that behavior in light of their subsequent lane-changing decisions. The data show a statistically defensible connection between these behaviors. These phenomena are not new, but highlight the importance of the data quality and of observing naturalistic driving behavior, and this article demonstrates a method to calibrate specific parameters related to the behavior. [ABSTRACT FROM PUBLISHER]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Intelligent Transportation Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/15472450.2017.1283224
    Languages:
      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 202
    Subjects:
      – SubjectFull: Traffic flow
        Type: general
      – SubjectFull: Traffic engineering software
        Type: general
      – SubjectFull: Automobile driving
        Type: general
      – SubjectFull: Computer software
        Type: general
    Titles:
      – TitleFull: Hardware and software for collecting microscopic trajectory data on naturalistic driving behavior.
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            NameFull: Shrestha, Deepak
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            NameFull: Lovell, David J.
      – PersonEntity:
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            NameFull: Tripodis, Yorghos
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            – D: 01
              M: 07
              Text: 2017
              Type: published
              Y: 2017
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              Value: 21
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              Value: 3
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            – TitleFull: Journal of Intelligent Transportation Systems
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