How do transportation professionals perceive the impacts of AI applications in transportation? A latent class cluster analysis

TL;DR

This study investigates how transportation professionals perceive the efficiency and equity impacts of AI applications in transportation. Using a U.S. survey (n = $354$, with $N=270$ complete for modeling) and latent class cluster analysis, the authors identify four latent segments: AI Neutral, AI Optimist, AI Pessimist, and AI Skeptic ($K=4$). The results show broad optimism about AI improving efficiency and traveler experience, but mixed views on equity and widespread concerns about AI ethics literacy and potential amplification of inequalities. The findings reveal significant associations between latent class membership and age, education level, and AI knowledge, underscoring the need for targeted education and outreach to improve readiness for AI-enabled transformation in the transportation sector.

Abstract

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.

Figures (3)

• Figure 1: Model structure of the latent class cluster analysis
• Figure 2: Mean of indicators of the four segments
• Figure 3: Sample histogram and cluster distributions of indicators (weighted N = 270)