A hybrid spatio-temporal deep learning framework for urban travel time prediction and intelligent routing: Integrating recurrent and graph neural networks
Accurate short-term travel-time forecasting is important for intelligent transportation systems, but reported gains can be misleading when temporal leakage, ambiguous graph definitions, normalized error units, weak baselines, or untested routing claims are not controlled. This study evaluates an integrated forecasting...