Leveraging Language Foundation Models for Human Mobility Forecasting

TL;DR

This paper introduces a novel approach that uses language foundation models for human mobility forecasting by transforming numerical sequences into natural language, enabling direct application of language models to predict visitor flows.

Contribution

The study presents a new pipeline, AuxMobLCast, integrating language models with auxiliary classification for improved mobility forecasting, a novel application of language models in this domain.

Findings

Pre-trained language models effectively forecast human mobility patterns.

The AuxMobLCast pipeline outperforms traditional numerical methods.

Empirical results on three datasets validate the approach's effectiveness.

Abstract

In this paper, we propose a novel pipeline that leverages language foundation models for temporal sequential pattern mining, such as for human mobility forecasting tasks. For example, in the task of predicting Place-of-Interest (POI) customer flows, typically the number of visits is extracted from historical logs, and only the numerical data are used to predict visitor flows. In this research, we perform the forecasting task directly on the natural language input that includes all kinds of information such as numerical values and contextual semantic information. Specific prompts are introduced to transform numerical temporal sequences into sentences so that existing language models can be directly applied. We design an AuxMobLCast pipeline for predicting the number of visitors in each POI, integrating an auxiliary POI category classification task with the encoder-decoder architecture. This research provides empirical evidence of the effectiveness of the proposed AuxMobLCast pipeline to discover sequential patterns in mobility forecasting tasks. The results, evaluated on three real-world datasets, demonstrate that pre-trained language foundation models also have good performance in forecasting temporal sequences. This study could provide visionary insights and lead to new research directions for predicting human mobility.