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

TL;DR

A GeoAI Hybrid analytical framework is proposed that sequentially integrates Multiscale Geographically Weighted Regression, Random Forest, and Spatio-Temporal Graph Convolutional Networks 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.

Abstract

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.

Figures (7)

• Figure 1: Normalised spatiotemporal traffic flow intensity heatmaps for (a) motor vehicle, (b) public transit, and (c) active transport, aggregated over all 350 zones. Rows = days of the week; columns = hours of the day. Colour intensity denotes normalised flow on $[0,1]$; bicubic interpolation applied for display.
• Figure 2: MGWR coefficient surfaces for (a) land use mix index ($\hat{\beta}_1$), (b) population density ($\hat{\beta}_2$), (c) transit accessibility ($\hat{\beta}_3$), and (d) road network density ($\hat{\beta}_4$), estimated for the weekday morning peak. Hatching marks zones where the pseudo-$t$ statistic is non-significant ($p>0.05$). Coordinates are normalised.
• Figure 3: Comparative model performance: (a) RMSE by mobility mode for six model families; (b) overall $R^{2}$ per model; (c) hourly MAPE profile over a 24-hour cycle for four selected models. Shaded band shows $\pm 1$ SD around GeoAI Hybrid predictions.
• Figure 4: Land use mix index (LUM; Eq. \ref{['eq:lum']}) against normalised traffic flow for (a) motor vehicle, (b) public transit, and (c) active transport. Symbol shape and colour distinguish zone typologies. Coloured lines show within-typology regression fits; dashed black line shows the overall trend. Pearson $r$ and $p$-value are inset.
• Figure 5: Spatiotemporal clustering: (a) DBSCAN spatial distribution with zone centroids (stars = cluster centres; grey crosses = noise); (b) mean normalised traffic profiles of five clusters over six-hourly intervals; (c) silhouette score versus cluster count $k$ for all three modes. Dashed red line marks optimal $k=5$.