The burgeoning field of Shared Autonomous Vehicles (SAVs) presents transformative potential for the transport sector, subject to public acceptance. Traditional acceptance models, primarily reliant on Structural Equation Modelling (SEM), often fall short of capturing the complex, non-linear dynamics underlying this acceptance. To address these limitations, this paper proposes a Machine Learning (ML) approach to predict public acceptance of SAVs and employs a chord diagram to visualize the influence of different predictors. This approach reveals nuanced, non-linear relationships between factors at both macro and micro levels, and identifies attitude as the primary predictor of SAV usage intention, followed by perceived risk, perceived usefulness, trust, and perceived ease of use. The framework also uncovers divergent perceptions of these factors among SAV adopters and non-adopters, providing granular insights for strategic initiatives to enhance SAV acceptance. Using SAV acceptance as a case study, our findings contribute a novel, machine learning-based perspective to the discourse on technology acceptance, underscoring the importance of nuanced, data-driven approaches in understanding and fostering public acceptance of emerging transport technologies.

翻译：共享自动驾驶汽车（SAVs）这一新兴领域正为交通行业带来变革性潜力，但其发展取决于公众接受度。传统接受度模型主要依赖结构方程模型（SEM），往往难以捕捉该接受度背后复杂的非线性动态特征。为弥补上述局限，本文提出基于机器学习（ML）的方法来预测公众对SAV的接受度，并采用弦图可视化不同预测因子的影响强度。该框架揭示了宏观与微观层面因素之间微妙的非线性关系，识别出态度是SAV使用意愿的首要预测因子，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接受者与非接受者对这些因素的认知差异，为制定提升SAV接受度的战略举措提供了精细化洞见。以SAV接受度作为案例研究，我们的研究结果为技术接受度理论提供了创新的机器学习视角，凸显了以数据驱动的精细化方法在理解与促进公众对新兴交通技术接受度方面的重要性。