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.

翻译：近年来，人工智能在交通领域的应用数量不断增加。作为一种新兴技术，人工智能在推动交通目标实现方面的潜力及其对交通行业的全面影响尚不明确。在交通领域探索这些议题的过程中，至关重要的是了解作为人工智能交通应用驱动力的交通专业人员如何看待其在效率和公平方面的潜在影响。为此，我们对美国的交通专业人员进行了调查，共收集了354份有效回复。基于调查结果，我们开展了描述性分析和潜在类别聚类分析。前者概述了交通专业人员的普遍态度，后者则根据他们对人工智能的潜在态度识别出不同群体。我们发现，人们对人工智能在改善交通效率、降低成本、提升出行体验等多方面的潜力普遍持乐观态度；然而，在人工智能促进交通公平的潜力方面，回复意见存在分歧。此外，许多受访者担心交通界对人工智能伦理的理解不足，且人工智能在交通领域的应用可能加剧现有不平等。通过潜在类别聚类分析，我们识别出四个潜在群体：人工智能中立者、人工智能乐观者、人工智能悲观者和人工智能怀疑者。潜在群体归属与受访者的年龄、教育水平和人工智能知识水平显著相关。总体而言，研究结果揭示了交通界整体在利用人工智能系统改变现有实践方面的准备程度，并为开展针对性教育以提升交通专业人员对人工智能的理解提供了依据。