用混合AI模型分析城市交通与土地利用的时空差异，提升多模式出行预测精度。
Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility
- 分步融合地理加权回归、随机森林与时空图卷积网络建模交通动态
- 模型误差比基准降低23%-62%，解释力达R²=0.891
- 识别出5类交通功能区，揭示城市形态对交通规律的决定性影响
城市交通流受土地利用布局与时空异质出行需求之间复杂非线性交互的驱动。传统全局回归和时间序列模型难以同时捕捉多尺度动态及多种出行方式的特征。本研究提出一种GeoAI混合分析框架，依次集成多尺度地理加权回归（MGWR）、随机森林（RF）与时空图卷积网络（ST-GCN），以建模三种出行模式（机动车、公共交通、慢行交通）的交通流时空异质性及其与土地利用的交互关系。基于覆盖六个城市、350个交通分析区的实证校准数据集，涵盖两种截然不同的城市形态，得出四项关键发现：(i) GeoAI混合框架实现均方根误差（RMSE）0.119，R²达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最强预测因子，公交站点密度是公共交通最强预测因子；(iii) DBSCAN聚类识别出五类功能型城市交通类型，轮廓系数0.71，且框架残差空间自相关（莫兰指数0.218，p<0.001），相比普通最小二乘法降低72%；(iv) 跨城市迁移实验显示，同类城市间具有中等可迁移性（R²≥0.78），但跨类别泛化能力有限，凸显城市形态背景的主导作用。该框架为规划者与交通工程师提供可解释、可扩展的工具，支持多模式出行管理与土地利用政策制定。
原文摘要 · Abstract (English)
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.
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