Browsing by Author "Higgs, Bryan James"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Application of Naturalistic Truck Driving Data to Analyze and Improve Car Following Models
    Higgs, Bryan James (Virginia Tech, 2011-12-02)
    This research effort aims to compare car-following models when the models are calibrated to individual drivers with the naturalistic data. The models used are the GHR, Gipps, Intelligent Driver, Velocity Difference, Wiedemann, and the Fritzsche model. This research effort also analyzes the Wiedemann car-following model using car-following periods that occur at different speeds. The Wiedemann car-following model uses thresholds to define the different regimes in car following. Some of these thresholds use a speed parameter, but others rely solely upon the difference in speed between the subject vehicle and the lead vehicle. This research effort also reconstructs the Wiedemann car-following model for truck driver behavior using the Naturalistic Truck Driving Study's (NTDS) conducted by Virginia Tech Transportation Institute. This Naturalistic data was collected by equipping 9 trucks with various sensors and a data acquisition system. This research effort also combines the Wiedemann car-following model with the GHR car-following model for trucks using The Naturalistic Truck Driving Study's (NTDS) data.
  • Thumbnail Image
    Emotional Impacts on Driver Behavior: An Emo-Psychophysical Car-Following Model
    Higgs, Bryan James (Virginia Tech, 2014-09-09)
    This research effort aims to create a new car-following model that accounts for the effects of emotion on driver behavior. This research effort is divided into eight research milestones: (1) the development of a segmentation and clustering algorithm to perform new investigations into driver behavior; (2) the finding that driver behavior is different between drivers, between car-following periods, and within a car-following period; (3) the finding that there are patterns in the distribution of driving behaviors; (4) the finding that driving states can result in different driving actions and that the same driving action can be the result of multiple driving states; (5) the finding that the performance of car-following models can be improved by calibration to state-action clusters; (6) the development of a psychophysiological driving simulator study; (7) the finding that the distribution of driving behavior is affected by emotional states; and (8) the development of a car-following model that incorporates the influence of emotions.