Investigation of the Role of Attitudinal Factors on Adoption of Emerging Automated Vehicle and Vehicle Safety Technologies
Investigating the Role of Attitudinal Factors on Adoption of Emerging Automated Vehicle and Vehicle Safety Technologies
Co-Principal Investigator: Michael Maness, Assistant Professor, Department of Civil & Environmental Engineering
Project Duration: 48 months
Project Budget (Federal UTC Funds): $220,000
Project Budget (Cost-share): $110,000
Institution: University of South Florida
Abstract
Emerging automotive and transportation technologies have created revolutionary possibilities in the way we might travel and drive in the future. These technologies include advanced vehicle safety technologies that are aimed at keeping the vehicle occupants safe and automated vehicles that can drive by themselves with little to no need for a human driver, and have the potential to revolutionize travel behavior. However, it is important to understand the effect that consumers’ attitudes will have on the adoption of these technologies and their ultimate impact on travel. The proposed project will result in the review and development of modeling methods that incorporate information about people’s attitudes and perceptions with socioeconomic data and unobserved heterogeneity. These modeling efforts will provide researchers and practitioners with documents to help familiarize them with the incorporation of attitudes and perceptions in the adoption of new technologies and their impact on travel behavior. The models developed and their accompanying software will be available for use by the public to help disseminate these methods into the larger transportation modeling community. Additionally, the project will collect data that will enable future analysis and development of models to forecast changes in perceptions due to social learning and social influence processes. The data collection effort will aid in providing guidance on how respondents are handling the task of relaying perceptions and travel choices. This will be important in understanding possible sources of survey error related to biases due to question structure and question ordering. This will be important for improving the efficiency and accuracy of TOMNET’s year 3 collective survey effort.