An Integrated Multimodal Deep Learning and Reinforcement Framework for Intelligent Travel Demand Forecasting

Abstract

Precise short-term travel demand forecasting and personalized trip advice are two of the most challenging problems in intelligent transportation systems in part due to the fact that traveler behavior is influenced concurrently by spatial configuration, time dynamics, socio-demographic environment, and cost sensitivity. The majority of the current models cover these dimensions separately. The current paper suggests an end-to-end, monolithic framework that pairs the learning of multimodal representations with a spatio-temporal graph neural network (ST-GNN), a Transformer-based forecasting module, a causal inference layer, and a Q-learning recommender. Heterogeneous travel history, such as demographics, trip purpose, mode of transport, cost of accommodation, and temporal attitudes are co-embedded and propagated through graph convolutions and multi-head self-attention to generate multi-horizon destination-level demand prediction. An actionable do-calculus layer measures the impact of traveler characteristics on decisions and the policy implications guide a reinforcement learning agent that modulates transport and accommodation suggestions to traveler groups. The ST-GNN + Transformer forecaster achieves a 25% lower RMSE than an ARIMA baseline on a publicly available dataset of approximately 8,700 traveler records and, in comparison to the LSTM and DCRNN alternatives, converges within just 300 training episodes. The framework provides a replicable base to adaptable, understandable model of travel behavior in smart-city and tourism analytics contexts.