人工智能驱动的交通流模式与土地利用互动的时空异质性：基于地理人工智能的多模态城市流动性分析

Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility

March 5, 2026

作者: Olaf Yunus Laitinen Imanov

cs.AI

摘要

城市交通流受土地利用配置与时空异质性出行需求之间复杂的非线性相互作用支配。传统全局回归与时间序列模型难以同步捕捉多交通方式的多尺度动态特征。本研究提出一种GeoAI混合分析框架，通过序贯整合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），分别对机动车、公共交通和主动出行三种交通模式的流量时空分异规律及其与土地利用的交互作用进行建模。将该框架应用于跨越两种对比城市形态的六个城市、包含350个交通分析区的实证校准数据集，得出四项核心发现：（1）GeoAI混合模型的均方根误差（RMSE）为0.119、R²达0.891，较基准模型性能提升23-62%；（2）SHAP分析显示土地利用混合度是机动车流量的最强预测因子，而公交站点密度对公共交通流量预测贡献最大；（3）DBSCAN聚类识别出五种功能迥异的城市交通类型（轮廓系数0.71），且GeoAI混合模型残差的莫兰指数降至0.218（p<0.001），较OLS基线降低72%；（4）跨城市迁移实验表明模型在聚类内部具有中等可迁移性（R²≥0.78），但跨聚类泛化能力有限，凸显城市形态背景的主导作用。该框架为规划师与交通工程师提供了可解释、可扩展的决策工具，支持基于实证的多模式交通管理与土地利用政策设计。

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.