A Universal Model for Human Mobility Prediction

TL;DR

This paper introduces UniMob, a universal model for human mobility prediction that unifies individual trajectory and crowd flow data using a diffusion transformer and alignment mechanism, outperforming existing methods especially in noisy scenarios.

Contribution

The paper presents a novel unified model that predicts both individual and collective human mobility data, bridging modal gaps with a multi-view tokenizer and alignment mechanism.

Findings

Outperforms state-of-the-art in trajectory and flow prediction.

Achieves over 14% MAPE and 25% Accuracy@5 improvements in noisy data.

Validates effectiveness on real-world datasets.

Abstract

Predicting human mobility is crucial for urban planning, traffic control, and emergency response. Mobility behaviors can be categorized into individual and collective, and these behaviors are recorded by diverse mobility data, such as individual trajectory and crowd flow. As different modalities of mobility data, individual trajectory and crowd flow have a close coupling relationship. Crowd flows originate from the bottom-up aggregation of individual trajectories, while the constraints imposed by crowd flows shape these individual trajectories. Existing mobility prediction methods are limited to single tasks due to modal gaps between individual trajectory and crowd flow. In this work, we aim to unify mobility prediction to break through the limitations of task-specific models. We propose a universal human mobility prediction model (named UniMob), which can be applied to both individual trajectory and crowd flow. UniMob leverages a multi-view mobility tokenizer that transforms both trajectory and flow data into spatiotemporal tokens, facilitating unified sequential modeling through a diffusion transformer architecture. To bridge the gap between the different characteristics of these two data modalities, we implement a novel bidirectional individual and collective alignment mechanism. This mechanism enables learning common spatiotemporal patterns from different mobility data, facilitating mutual enhancement of both trajectory and flow predictions. Extensive experiments on real-world datasets validate the superiority of our model over state-of-the-art baselines in trajectory and flow prediction. Especially in noisy and scarce data scenarios, our model achieves the highest performance improvement of more than 14% and 25% in MAPE and Accuracy@5.