Translating Human Mobility Forecasting through Natural Language Generation

TL;DR

This paper introduces a novel approach to human mobility forecasting by framing it as a language translation task, using a sequence-to-sequence model to generate future mobility descriptions from current data.

Contribution

The paper proposes a new translation-based framework, SHIFT, that models mobility forecasting as natural language generation, integrating semantic context for improved predictions.

Findings

SHIFT outperforms traditional models on real-world datasets.

The translation approach effectively incorporates contextual information.

The method demonstrates a new paradigm in mobility forecasting.

Abstract

Existing human mobility forecasting models follow the standard design of the time-series prediction model which takes a series of numerical values as input to generate a numerical value as a prediction. Although treating this as a regression problem seems straightforward, incorporating various contextual information such as the semantic category information of each Place-of-Interest (POI) is a necessary step, and often the bottleneck, in designing an effective mobility prediction model. As opposed to the typical approach, we treat forecasting as a translation problem and propose a novel forecasting through a language generation pipeline. The paper aims to address the human mobility forecasting problem as a language translation task in a sequence-to-sequence manner. A mobility-to-language template is first introduced to describe the numerical mobility data as natural language sentences. The core intuition of the human mobility forecasting translation task is to convert the input mobility description sentences into a future mobility description from which the prediction target can be obtained. Under this pipeline, a two-branch network, SHIFT (Translating Human Mobility Forecasting), is designed. Specifically, it consists of one main branch for language generation and one auxiliary branch to directly learn mobility patterns. During the training, we develop a momentum mode for better connecting and training the two branches. Extensive experiments on three real-world datasets demonstrate that the proposed SHIFT is effective and presents a new revolutionary approach to forecasting human mobility.