Public acceptance is critical to the adoption of Shared Autonomous Vehicles (SAVs) in the transport sector. Traditional acceptance models, primarily reliant on Structural Equation Modeling, may not adequately capture the complex, non-linear relationships among factors influencing technology acceptance and often have limited predictive capabilities. This paper introduces a framework that combines Machine Learning techniques with chord diagram visualizations to analyze and predict public acceptance of technologies. Using SAV acceptance as a case study, we applied a Random Forest machine learning approach to model the non-linear relationships among psychological factors influencing acceptance. Chord diagrams were then employed to provide an intuitive visualization of the relative importance and interplay of these factors at both factor and item levels in a single plot. Our findings identified Attitude as the primary predictor of SAV usage intention, followed by Perceived Risk, Perceived Usefulness, Trust, and Perceived Ease of Use. The framework also reveals divergent perceptions between SAV adopters and non-adopters, providing insights for tailored strategies to enhance SAV acceptance. This study contributes a data-driven perspective to the technology acceptance discourse, demonstrating the efficacy of integrating predictive modeling with visual analytics to understand the relative importance of factors in predicting public acceptance of emerging technologies.

翻译：公众接受度对于共享自动驾驶汽车（SAVs）在交通领域的采纳至关重要。传统的接受度模型主要依赖结构方程建模，可能无法充分捕捉影响技术接受度的因素之间复杂的非线性关系，且预测能力往往有限。本文提出一个结合机器学习技术与弦图可视化的框架，用以分析和预测公众对技术的接受度。以SAV接受度作为案例研究，我们应用随机森林机器学习方法，对影响接受度的心理因素之间的非线性关系进行建模。随后，我们采用弦图在一个单一图表中，直观地展示了这些因素在因子层面和项目层面的相对重要性及其相互作用。我们的研究发现，态度是预测SAV使用意向的主要因素，其次是感知风险、感知有用性、信任和感知易用性。该框架还揭示了SAV采纳者与非采纳者之间的认知差异，为制定提升SAV接受度的针对性策略提供了见解。本研究为技术接受度讨论贡献了一个数据驱动的视角，证明了将预测建模与可视化分析相结合，在理解预测公众对新兴技术接受度的因素相对重要性方面的有效性。