Learning and Risk Perception Mechanisms in Route Choice and Activity Scheduling Dynamics

Abstract

This dissertation explores the learning and risk mechanisms underlying the dynamics of route choice and activity scheduling decisions. With respect to route choice dynamics, the study models decision mechanisms related to travel time perception, learning, and risk attitudes, exploring their implications on system performance over time. This objective is accomplished by performing experiments using a network performance model, in this case an agent-based simulation model of individual experience given the collective effects arising from the interaction of the agents' route choice decisions. In regards to activity scheduling decisions, the study examines the range of behavioral insights obtained from a modeling framework that views the individual scheduling process as a single-server queuing system, introducing the concept of activity stress. The study presents numerical experiments on this framework using a discrete event simulation of an M/G/1 queuing system. Furthermore, an operational model of activity participation is estimated using observed activity schedules. The results indicate that travel time uncertainty and user perception of this uncertainty greatly affect the performance of the system over time, in particular the convergence of traffic flows. With respect to activity scheduling, the results overall indicate the significance of activity stress in motivating activity scheduling and participation decisions over time, with particular importance placed on the evolution of activity queue and activity schedule states over time. Results from studies investigating both route choice and activity scheduling behavior indicate the important role of decision dynamics for determining the behavior of users in complex information-rich environments.