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

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Bibliographic Details
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]
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Database: Engineering Source
Description
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]
ISSN:15472450
DOI:10.1080/15472450.2017.1283224