A Comparison of Online and In-Person Activity Engagement: The Case of Shopping and Eating Meals

You're viewing a past Journal from the Good Systems Grand Challenge team at The University of Texas at Austin from February 2020.Office of the VP for Researc

Attitudes and Behaviors Causal Relationships: Uncovering Latent Segments Within a Heterogeneous Population

69A3551747116This project aimed at unraveling the contemporaneous relationship that exists between attitudes and choice behaviors. Attitudes, perceptions, and preferences may shape behaviors; likewise, behavioral choices exercised by individuals may offer experiences that shape attitudes. While it is likely that these relationships play out over time, the question of whether attitudes affect behaviors or behaviors affect attitudes at a specific cross-section in time remains unanswered and a fruitful area of inquiry. Various studies in the literature have explored this question, but have done so without explicitly recognizing the heterogeneity that may exist in the population. In other words, the causal structure at play at any point in time may differ across individuals, thus motivating the development of an approach that can account for the presence of multiple segments in the population, each following a different causal structure. Results suggest that there is considerable heterogeneity in the population with the contemporaneous causal structures in which behaviors shape attitudes more prevalent than those in which attitudes affect choice behaviors. These findings have important implications for transport modeling and policy development

A Comparison of Online and In-Person Activity Engagement: The Case of Shopping and Eating Meals

You are viewing a publication by UT affiliates from the May 2020 Good System Network Digest.Office of the VP for Researc

Attitudes Towards Emerging Mobility Options and Technologies \u2013 Phase 3: Survey Data Compilation, and Analysis

69A3551747116Emerging transportation technologies including electric and autonomous vehicles and emerging mobility services such as ride-hailing and vehicle sharing are bringing about transformative changes in the transportation landscape. With the emergence of new transportation technologies and services, it is critical that transportation forecasting models be enhanced to account for behavioral dynamics that will result from the increasing penetration of disruptive forces in the transportation marketplace. To enhance transportation forecasting models, people\u2019s attitudes towards and perceptions of emerging technologies and services need to be measured and understood. Armed with such an understanding, it will be possible to specify and develop behavioral models that account for attitudes and perceptions, adoption cycles, and adaptation patterns. It is envisioned that such models will help decision-makers better plan transportation infrastructure systems and design marketing and policy strategies that maximize the benefits of these disruptive technologies. With this background, the T4 survey (TOMNET Transformative Transportation Technologies Survey) has been designed and deployed in the previous stages of this large-scale survey based research projects. The survey intended to collect very detailed and in-depth data about people\u2019s mobility patterns, as well as attitudes towards and perceptions of emerging transportation options such as ridehailing services and autonomous vehicles. The third phase of this research project focuses on the compilation and analysis of survey data in order to better understand people\u2019s preferences and choices for future mobility options and technologies in the Greater Phoenix metropolitan area. A comprehensive description of all the steps taken to full deployment, data cleaning, and weighting is provided, in addition to a descriptive weighted univariate illustration of the findings from the Phoenix survey sample