Hybrid Deep Learning Architecture for Nationwide Travel Behavior Prediction from Satellite Imagery

Nationwide travel behavior data are important for transportation planning, but household travel surveys are costly, infrequent, and difficult to implement consistently across diverse regions. This study develops a two-stage hybrid learning framework for estimating county-level travel behavior from satellite imagery. Stage 1 uses a gradient-boost-enhanced encoder–decoder (GBEED) to infer 26 demographic variables from Landsat 8 imagery, while Stage 2 uses a metalearning gradient boost (MLGB) model to estimate 17 travel behavior indicators from the inferred demographic profile. The framework was developed and evaluated using 3,132 US counties, 2022 American Community Survey estimates, and geographically balanced training, validation, and test partitions. GBEED achieved an average R 2 of 0.867 and outperformed encoder–decoder, ResNet-50, vision transformer, transfer-learning CNN, and DenseNet-style baselines. Under end-to-end evaluation, in which Stage 2 receives demographic variables inferred by GBEED rather than observed ACS demographics, MLGB achieved an average R 2 of 0.934 and an average PET-20 of 62.3%. It outperformed the evaluated conventional regression, tree-based, boosting, and transportation-specific baseline models. The geographic transferability index remained above 0.86 for Stage 1 and 0.90 for Stage 2 across the evaluated regional cluster configurations. Zero-shot evaluation in Puerto Rico showed stronger transfer for aggregate indicators than for sparse and equity-sensitive outcomes such as zero-vehicle households, taxicab use, and bicycle use. SHAP analysis indicated that population scale, age structure, racial and ethnic composition, educational attainment, income, and inequality contributed to the travel behavior predictions. The results demonstrate the potential of satellite imagery for scalable and interpretable county-level travel behavior screening, while sparse and equity-sensitive outputs require local validation before project-level planning or investment decisions.