Hybrid Deep Learning and Machine Learning Framework for Intelligent Travel Demand Forecasting

Abstract

Almost all operations of a smart transportation or tourism platform are based on reliable travel demand forecasts, including fleet allocation and dynamic pricing. The information that is now at hand to do the task, trip records, accommodation logs, demographic surveys, are large and heterogeneous, a combination of spatial structure and long-range temporal dependence with sequential behavior and tabular properties that cannot be handled by any single model family. In this paper, an intelligent framework is developed as a hybrid one, where four complementary learners are used to operate in parallel: a spatio-temporal graph neural network (ST-GNN) to represent dependencies between travel zones, a Transformer to represent long-horizon temporal patterns, an LSTM to represent sequential mobility dynamics, and an XGBoost to represent structured demographic and cost characteristics. A stacked meta-learner amalgamates the four branch predictions, and a Q-learning agent transforms predictions into adaptive transport and accommodation suggestions with a reward based on satisfaction-minus-cost. The fused model, tested on the New York City TLC taxi-trip corpus with demographic information, achieves lower RMSE than either ARIMA (0.214) or the best single learner (0.169) and higher R² (0.91), and the policy recommendation policy converges in about 300 episodes. In addition to accuracy, the framework provides a formal fusion architecture, a full algorithmic specification, and a statistical validation protocol, and provides a scalable and interpretable platform to intelligent mobility analytics.