Exploring Travel Behavior Change Opportunities Through a Spatial-Temporal Dimensioned Persuasion Framework

Abstract

There is no dispute that making a behavioral change is an arduous process. Planners and policy makers have been attempting to deploy travel demand management strategies in most metropolitan regions to eliminate or temporally and spatially shift trips away from congested periods and corridors. However, these existing strategies have not been moving the needle fast enough, as there still exist many factors that hinder travel behavioral change. With the global proliferation of mobile technology, smartphones have become widely utilized as tools of TDM strategies. This mobile framework allows for agencies and researchers to intervene and deliver timely information to influence individual-user behavior. The ability to collect and disseminate location-based information including trips, travel time, money spent, activities conducted, etc., through this platform has created the opportunity to make these interventions more personalized. Despite the fact that attitudinal theories from social psychology have been quite extensively applied to the study of user intention and behavior, these theories have been developed for predicting user acceptance of information technology rather than providing systematic analysis and design methods to develop persuasive solutions. A persuasive solution understands significant behavioral changes are hard, if not impossible, to make. Therefore, it attempts to understand a user’s personality, daily patterns, and lifestyle, in order to help her get onboard by using a series of stepping stones and also sparking motivation using personalized incentives. This dissertation frames an incentive-based travel behavior change strategy intended to change the departure time of individuals. For achieving this, the research in hand develops a new methodology for capturing important locations of individuals using their GPS point traces and location based social network databases. Moreover, it introduces user-activity networks, constructed of individuals and their important visited locations to categorize the lifestyles of individuals by performing graph partitioning on the user-activity networks. In order to evaluate the elasticity of incentive, a hazard model is developed to examine minute change in departure time while offering incentive and taking different trip purposes into consideration. At the end to create and examine the idea of personas in transportation-realted behavioral change, this study tries to create clusters of people called personas. These personas are shaped based on observed historical behavior, flexibility in time, and lifestyle, and their socio-demographic status described. Next, 24 users from the personas were selected and an intervention scenario on changing departure time was conducted.