Deep Generative Models for Trajectory Prediction and Mobility Network Forecasting

Abstract

Predicting human mobility is essential for urban planning, traffic management, and epidemiology. This thesis tackles two intertwined challenges: accurately forecasting individual trajectories and inferring the resulting mobility network. First, we introduce TrajLearn, a Transformer‑based deep generative model that treats trajectories as token sequences and employs spatially constrained beam search to predict each individuals’s next k locations with high precision. Building on these forecasts, we present MobiNetForecast, which constructs and predicts the future topology of the mobility network by detecting when independently predicted trajectories intersect in space and time. Across large, real‑world datasets, our unified framework achieves up to 40% relative gains in trajectory accuracy and up to 100x improvement in contact prediction over state-of-the-art baselines. These results demonstrate that combining advanced sequence modeling with explicit contact inference offers a powerful, scalable solution for dynamic mobility network forecasting.