Modeling Driver Behavior in Work and Nonwork Zones: Multidimensional Psychophysical Car-Following FrameworkAuthor/Presenter: Lochrane, Taylor W. P.; Al-Deek, Haitham; Dailey, Daniel J.; Krause, Cory
A new multidimensional framework for modeling car following on the basis of statistical evaluation of driver behavior in work and nonwork zones is presented. The models developed as part of this multidimensional framework use psychophysical concepts for car following that are close in character to the Wiedemann model used in popular traffic simulation software such as VISSIM. The authors hypothesized that with an instrumented research vehicle (IRV) in a living laboratory (LL) along a roadway, the parameters of models developed from the multidimensional framework could be derived statistically and calibrated for driver behavior in work zones. This hypothesis was validated with data collected from a group of 64 random participants who drove the IRV through an LL set up along a work zone on I-95 near Washington, D.C. For this validation, the IRV was equipped with sensors, including radar, and an onboard data collection system to record the vehicle performance. One of the limitations of current car-following models is that they account for only one overall behavioral condition. This study demonstrated that there are four different categories of car-following behavior models, each with different parameter distributions: the four categories are divided by traffic condition (congested versus noncongested) and by roadway condition (work versus nonwork zone). Calibrated threshold values for each of these four categories are presented. Furthermore, this new framework for modeling car-following behavior is described in a multidimensional setting and can be used to enhance vehicle behavior in microsimulation models.
Publisher: Transportation Research Board
Publication Date: 2015
Full Text URL: Link to URL
Publication Types: Books, Reports, Papers, and Research Articles
Topics: Behavior; Traffic Models; Vehicle Following; Work Zones