Computational methods for investigating intradriver heterogeneity using vehicle trajectory data

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Publication Type thesis
School or College College of Engineering
Department Civil & Environmental Engineering
Author Taylor, Jeffrey D.
Title Computational methods for investigating intradriver heterogeneity using vehicle trajectory data
Date 2014-08
Description Traffic simulations, which attempt to describe how individual vehicles move on road segments in a network, rely on mathematical traffic flow models developed from empirical vehicle trajectory data (position, speed, acceleration, etc.). Many of these microscopic traffic flow models are described as car-following models, which assume that a driver will respond to the actions of the driver/s or vehicle/s located in front of them (stimulus-response behavior). Model calibration can be performed using regression and/or optimization techniques, but the process is often complicated by uncertainty and variation in human behavior, which can be described as driver heterogeneity. Driver heterogeneity is conceptually based on the idea that different drivers may have different reactions to the same stimuli (interdriver heterogeneity), and an individual driver may react differently to the same type of stimulus (intradriver heterogeneity). To capture interdriver heterogeneity, car-following model parameters must be estimated for each driver/vehicle in the dataset, which are then used to describe a probability distribution associated with those model parameters. Capturing intradriver heterogeneity requires going one step further, calculating those same model parameters over much smaller time periods (i.e., seconds, or fractions of sections) within one vehicle's trajectory. This significantly reduces the amount of data available for calibration, limiting the ability to use traditional calibration procedures.
Type Text
Publisher University of Utah
Subject Car following model; Driver behavior; Dynamic time warping; Heterogeneity; NGSIM
Dissertation Institution University of Utah
Dissertation Name Master of Science
Language eng
Rights Management Copyright © Jeffrey D. Taylor 2014
Format Medium application/pdf
Format Extent 1,178,213 bytes
Identifier etd3/id/3159
ARK ark:/87278/s6sb7dz8
Setname ir_etd
Date Created 2014-10-29
Date Modified 2018-03-29
ID 196725
Reference URL