Accurately quantifying urban human mobility is crucial for tackling challenges in traffic engineering, urban planning and public health. Traditional static gravity models (GModel) often fail to address spatial heterogeneity and non-linear relationships in mobility flows, particularly in complex urban regions with new towns and metropolitan areas. This study investigates mobility flows in the Wuhan Metropolitan Area by introducing an Intelligent Gravity Model (IGModel) that integrates the theoretical insights of gravity models with the non-linear predictive capacity of LightGBM. The IGModel extends the gravity model by incorporating built environment and geometric variables while leveraging machine learning to enhance flow predictions. Through this hybrid approach, the IGModel achieves robust predictive performance (R-squared = 0.97) and provides interpretable insights using SHAP (Shapley Additive Explanations) analysis. The results demonstrate the complementary strengths of mechanism-driven and data-driven approaches, with the IGModel outperforming dynamic gravity models (DGModel) and offering actionable insights for urban planning and transportation management.

中文翻译：

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使用 LightGBM 增强重力模型预测城市交通模式：来自武汉都会区的见解

准确量化城市人口流动性对于应对交通工程、城市规划和公共卫生方面的挑战至关重要。传统的静态重力模型 （GModel） 通常无法解决流动性流中的空间异质性和非线性关系，尤其是在具有新城镇和大都市地区的复杂城市地区。本研究通过引入智能重力模型 （IGModel） 来调查武汉都会区的流动性流动，该模型将重力模型的理论见解与 LightGBM 的非线性预测能力相结合。IGModel 通过整合建筑环境和几何变量来扩展重力模型，同时利用机器学习来增强流量预测。通过这种混合方法，IGModel 实现了稳健的预测性能（R 平方 = 0.97），并使用 SHAP（Shapley 加法解释）分析提供可解释的见解。结果证明了机制驱动和数据驱动方法的互补优势，IGModel 的性能优于动态重力模型 （DGModel），并为城市规划和交通管理提供了可作的见解。