城市交通流受土地利用配置与时空异质出行需求之间复杂非线性交互作用的支配。传统的全球回归模型和时间序列模型无法同时捕捉跨多种出行方式的多尺度动态特征。本研究提出一种GeoAI混合分析框架，依次整合多尺度地理加权回归（MGWR）、随机森林（RF）和时空图卷积网络（ST-GCN），以建模机动车、公共交通和主动出行三种方式下交通流模式的时空异质性及其与土地利用的交互作用。将该框架应用于涵盖两种对比鲜明城市形态的六座城市中350个交通分析区的实证校准数据集，得出四项主要发现：(i) GeoAI混合模型的均方根误差（RMSE）为0.119，决定系数（R^2）为0.891，优于所有基准模型23-62%；(ii) SHAP分析表明，土地利用混合度是机动车流量的最强预测因子，而公交站点密度是公共交通的最强预测因子；(iii) DBSCAN聚类识别出五种功能迥异的城市交通类型，轮廓系数为0.71，且GeoAI混合模型的残差Moran's I指数为0.218（p<0.001），较OLS基线降低72%；(iv) 跨城市迁移实验显示，簇内迁移性中等（R^2>=0.78），但跨簇泛化能力有限，凸显了城市形态背景的主导作用。该框架为规划者和交通工程师提供了可解释、可扩展的工具包，用于支持循证的多模态出行管理和土地政策设计。

Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobility demand. Conventional global regression and time-series models cannot simultaneously capture these multi-scale dynamics across multiple travel modes. This study proposes a GeoAI Hybrid analytical framework that sequentially integrates Multiscale Geographically Weighted Regression (MGWR), Random Forest (RF), and Spatio-Temporal Graph Convolutional Networks (ST-GCN) to model the spatiotemporal heterogeneity of traffic flow patterns and their interaction with land use across three mobility modes: motor vehicle, public transit, and active transport. Applying the framework to an empirically calibrated dataset of 350 traffic analysis zones across six cities spanning two contrasting urban morphologies, four key findings emerge: (i) the GeoAI Hybrid achieves a root mean squared error (RMSE) of 0.119 and an R^2 of 0.891, outperforming all benchmarks by 23-62%; (ii) SHAP analysis identifies land use mix as the strongest predictor for motor vehicle flows and transit stop density as the strongest predictor for public transit; (iii) DBSCAN clustering identifies five functionally distinct urban traffic typologies with a silhouette score of 0.71, and GeoAI Hybrid residuals exhibit Moran's I=0.218 (p<0.001), a 72% reduction relative to OLS baselines; and (iv) cross-city transfer experiments reveal moderate within-cluster transferability (R^2>=0.78) and limited cross-cluster generalisability, underscoring the primacy of urban morphological context. The framework offers planners and transportation engineers an interpretable, scalable toolkit for evidence-based multimodal mobility management and land use policy design.