Marketing

Engineering

Transport engineering

Microeconomics

Public transport

Archaeology

Operating system

Economics

Computer science

Timeline

Mode choice

Business

Willingness to pay

Physics

Travel behavior

Preference

Politics

Optics

Geography

Political science

Focus (optics)

Public opinion

Human–computer interaction

Mode (computer interface)

Multinomial logistic regression

Machine learning

Technological Forecasting and Social Change

Rapid advances in technologies have accelerated the timeline for public use of fully-automated and communications-connected vehicles. Public opinion on self-driving vehicles or AVs is evolving rapidly, and many behavioral questions have not yet been addressed. This study emphasizes AV mode choices, including Americans’ willingness to pay (WTP) to ride with a stranger in a shared AV fleet vehicle on various trip types and the long-distance travel impacts of AVs. Exactly 2,588 complete responses to a stated-preference survey with 70 questions provide valuable insights on privacy concerns, safety and dynamic ride-sharing with strangers, long-distance travel and preferences for smarter vehicles and transport systems. Two hurdle models (which allow for a high share of zero-value responses) were estimated: one to predict WTP to share a ride and another to determine WTP to anonymize location while using AVs, and a multinomial logit was developed to estimate long-distance mode choices with AVs and SAVs available. Results suggest that WTP to share rides will rise over time, for a variety of reasons, and SAV use will be particularly popular for long-distance business travel. Elasticity estimates suggest that privacy may not be an important concern for AV-based travel.

Modeling Americans’ autonomous vehicle preferences: A focus on dynamic ride-sharing, privacy &amp; long-distance mode choices

Modeling Americans’ autonomous vehicle preferences: A focus on dynamic ride-sharing, privacy &amp; long-distance mode choices

Modeling Americans’ autonomous vehicle preferences: A focus on dynamic ride-sharing, privacy & long-distance mode choices