  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.
