Enhancing the understanding of passenger satisfaction in public transportation is crucial for operators to refine transit services and to establish and elevate quality standards. While many researchers have tackled this issue using diverse tools and methods, the prevalent approach involves surveys with discrete choice models or structural equations. However, a common limitation of these models lies in their inherent assumptions and predefined relationships between dependent and independent variables.

中文翻译：

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使用机器学习模型预测公共交通乘客满意度

加强对公共交通乘客满意度的了解对于运营商完善交通服务以及建立和提高质量标准至关重要。虽然许多研究人员使用不同的工具和方法解决了这个问题，但普遍的方法是使用离散选择模型或结构方程进行调查。然而，这些模型的共同局限性在于其固有假设以及因变量和自变量之间的预定义关系。