Title page for ETD etd-12122011-152121


Type of Document Master's Thesis
Author Higgs, Bryan James
Author's Email Address wuhbaam@vt.edu
URN etd-12122011-152121
Title Application of Naturalistic Truck Driving Data to Analyze and Improve Car Following Models
Degree Master of Engineering
Department Civil Engineering
Advisory Committee
Advisor Name Title
Abbas, Montasir M. Committee Chair
Guo, Feng Committee Member
Medina, Alejandra Committee Member
Keywords
  • GHR Model
  • Wiedemann Model
  • Car Following
  • Naturalistic Data
Date of Defense 2011-12-02
Availability unrestricted
Abstract
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.
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