Autonomous taxi services represent a transformative advancement in urban mobility, offering safety, efficiency, and round-the-clock operations. While existing literature has explored user acceptance of autonomous taxis through stated preference experiments and hypothetical scenarios, few studies have investigated actual user behavior based on operational AV services. This study addresses that gap by leveraging survey data from Wuhan, China, where Baidu's Apollo Robotaxi service operates at scale. We design a realistic survey incorporating actual service attributes and collect 336 valid responses from actual users. Using Structural Equation Modeling, we identify six latent psychological constructs, namely Trust \& Policy Support, Cost Sensitivity, Performance, Behavioral Intention, Lifestyle, and Education. Their influences on adoption behavior, measured by the selection frequency of autonomous taxis in ten scenarios, are examined and interpreted. Results show that Cost Sensitivity and Behavioral Intention are the strongest positive predictors of adoption, while other latent constructs play more nuanced roles. The model demonstrates strong goodness-of-fit across multiple indices. Our findings offer empirical evidence to support policymaking, fare design, and public outreach strategies for scaling autonomous taxis deployments in real-world urban settings.

翻译：自动驾驶出租车服务代表了城市交通领域的革命性进步，提供安全、高效且全天候的运营服务。现有文献虽已通过陈述偏好实验和假设场景探讨了用户对自动驾驶出租车的接受度，但基于实际运营自动驾驶服务研究真实用户行为的成果仍较为有限。本研究通过利用中国武汉百度Apollo Robotaxi大规模运营区域的调查数据填补了这一空白。我们设计了包含实际服务属性的现实性调查，收集了336份真实用户的有效问卷。运用结构方程模型，我们识别出六个潜在心理构念：信任与政策支持、成本敏感性、性能感知、行为意向、生活方式及教育背景，并考察和阐释了这些构念对采用行为（以十种场景中自动驾驶出租车的选择频次为测量指标）的影响机制。结果表明，成本敏感性和行为意向是预测采用行为的最强正向因素，其他潜在构念则发挥更为微妙的作用。该模型在多项拟合指数上均表现出优异的适配度。本研究为现实城市环境中规模化部署自动驾驶出租车的政策制定、票价设计与公众推广策略提供了实证依据。