טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
Ph.D Thesis
Ph.D StudentEran Ben-Elia
SubjectBehavioral Insights in Route-Choice Models with
Real Time Information
DepartmentDepartment of Civil and Environmental Engineering
Supervisors Full Professor Shiftan Yoram
Full Professor Erev Ido
Full Thesis textFull thesis text - English Version


Abstract

Advanced Travel Information Systems (ATIS) are designed to provide real time information enabling drivers to choose efficiently among routes and save travel time.  Travel demand modelers have been trying to analyze drivers' response to such systems usually under the assumptions of rational behavior and a utility maximization paradigm. Psychological research suggests that people do not intuitively maximize utility rather they are more sensitive to relative outcomes. Thus, route-choice models can be improved by adding realistic behavioral assumptions. However, different behavioral generalizations imply deviations from the predictions of utility maximization in different directions. Specifically, different choices arise when decisions are taken on the basis of descriptive information compared to those taken on the basis of personal experience. An experimental study of route choices was set up to investigate the combined effects of information and experience on route-choice decisions in a simulated environment of a simple two route network, whereby the participants can rely on a description of travel time variability (by viewing travel time ranges) and at the same time can rely also on personal experience through feedback of their chosen route's travel time. The results show that the effect of information is positive and more evident when participants lack long-term experience on the distributions of travel times. Furthermore, information seems to increase initial risk seeking behavior, reduce initial exploration and contribute to between subject risk-attitudes differences. Based on this data we estimated several advanced discrete choice models using Mixed Logit with panel data specifications. The model estimation results shows that non-informed participants tend to rely more on recent outcomes and are more sensitive to travel times, less sensitive to travel time variability and show tendency towards risk aversion. In contrast, informed participants are more aware of past sequence of outcomes, are more sensitive to travel time variability and have some inclination towards risk-seeking behavior. These findings have implications for cost-effective ATIS design especially in the conditions characterized by non recurrent congestion. More research is necessary to better understand the behavioral impacts of informed users on the general equilibrium of transport networks especially the effect of driver interaction and joint decision making effects.