  Recent years have witnessed an increasing number of artificial intelligence
(AI) applications in transportation. As a new and emerging technology, AI's
potential to advance transportation goals and the full extent of its impacts on
the transportation sector is not yet well understood. As the transportation
community explores these topics, it is critical to understand how
transportation professionals, the driving force behind AI Transportation
applications, perceive AI's potential efficiency and equity impacts. Toward
this goal, we surveyed transportation professionals in the United States and
collected a total of 354 responses. Based on the survey responses, we conducted
both descriptive analysis and latent class cluster analysis (LCCA). The former
provides an overview of prevalent attitudes among transportation professionals,
while the latter allows the identification of distinct segments based on their
latent attitudes toward AI. We find widespread optimism regarding AI's
potential to improve many aspects of transportation (e.g., efficiency, cost
reduction, and traveler experience); however, responses are mixed regarding
AI's potential to advance equity. Moreover, many respondents are concerned that
AI ethics are not well understood in the transportation community and that AI
use in transportation could exaggerate existing inequalities. Through LCCA, we
have identified four latent segments: AI Neutral, AI Optimist, AI Pessimist,
and AI Skeptic. The latent class membership is significantly associated with
respondents' age, education level, and AI knowledge level. Overall, the study
results shed light on the extent to which the transportation community as a
whole is ready to leverage AI systems to transform current practices and inform
targeted education to improve the understanding of AI among transportation
professionals.
