Dynamic Learning and Human Interactions under the Extended Belief-Desire-Intention Framework for Transportation Systems
type: CreativeWork
description: In recent years, multi-agent traffic simulation has been widely used to accurately evaluate the performance of a road network considering individual and dynamic movements of vehicles under a virtual roadway environment. Given initial traffic demands and road conditions, the simulation is executed with multiple iterations and provides users with converged roadway conditions for the performance evaluation. For an accurate traffic simulation model, the driver's learning behavior is one of the major components to be concerned, as it affects road conditions (e.g., traffic flows) at each iteration as well as performance (e.g., accuracy and computational efficiency) of the traffic simulation. The goal of this study is to propose a realistic learning behavior model of drivers concerning their uncertain perception and interactions with other drivers. The proposed learning model is based on the Extended Belief-Desire-Intention (E-BDI) framework and two major decisions arising in the field of transportation (i.e., route planning and decision-making at an intersection). More specifically, the learning behavior is modeled via a dynamic evolution of a Bayesian network (BN) structure. The proposed dynamic learning approach considers three underlying assumptions: 1) the limited memory of a driver, 2) learning with incomplete observations on the road conditions, and 3) non-stationary road conditions. Thus, the dynamic learning approach allows driver agents to understand real-time road conditions and estimate future road conditions based on their past knowledge. In addition, interaction behaviors are also incorporated in the E-BDI framework to address influences of interactions on the driver's learning behavior. In this dissertation work, five major human interactions adopted from a social science literature are considered: 1) accommodation, 2) collaboration, 3) compromise, 4) avoidance, and 5) competition. The first three interaction types help to mimic information exchange behavior…
abstract: In recent years, multi-agent traffic simulation has been widely used to accurately evaluate the performance of a road network considering individual and dynamic movements of vehicles under a virtual roadway environment. Given initial traffic demands and road conditions, the simulation is executed with multiple iterations and provides users with converged roadway conditions for the performance evaluation. For an accurate traffic simulation model, the driver's learning behavior is one of the major components to be concerned, as it affects road conditions (e.g., traffic flows) at each iteration as well as performance (e.g., accuracy and computational efficiency) of the traffic simulation. The goal of this study is to propose a realistic learning behavior model of drivers concerning their uncertain perception and interactions with other drivers. The proposed learning model is based on the Extended Belief-Desire-Intention (E-BDI) framework and two major decisions arising in the field of transportation (i.e., route planning and decision-making at an intersection). More specifically, the learning behavior is modeled via a dynamic evolution of a Bayesian network (BN) structure. The proposed dynamic learning approach considers three underlying assumptions: 1) the limited memory of a driver, 2) learning with incomplete observations on the road conditions, and 3) non-stationary road conditions. Thus, the dynamic learning approach allows driver agents to understand real-time road conditions and estimate future road conditions based on their past knowledge. In addition, interaction behaviors are also incorporated in the E-BDI framework to address influences of interactions on the driver's learning behavior. In this dissertation work, five major human interactions adopted from a social science literature are considered: 1) accommodation, 2) collaboration, 3) compromise, 4) avoidance, and 5) competition. The first three interaction types help to mimic information exchange behavior…
author: Kim, Sojung
language: en_US
keywords: Bayesian network, Belief-Desire-Intention, Dilemma zone, Driver's behavior, En route planning, Systems & Industrial Engineering, Agent-based simulation
date published: 2015
date modified: 2026-07-24T00:55:10Z
provider: University of Arizona
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Arizona.

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10150/578837