  Considering the multimodal nature of transport systems and potential
cross-modal correlations, there is a growing trend of enhancing demand
forecasting accuracy by learning from multimodal data. These multimodal
forecasting models can improve accuracy but be less practical when different
parts of multimodal datasets are owned by different institutions who cannot
directly share data among them. While various institutions may can not share
their data with each other directly, they may share forecasting models trained
by their data, where such models cannot be used to identify the exact
information from their datasets. This study proposes an Unsupervised Knowledge
Adaptation Demand Forecasting framework to forecast the demand of the target
mode by utilizing a pre-trained model based on data of another mode, which does
not require direct data sharing of the source mode. The proposed framework
utilizes the potential shared patterns among multiple transport modes to
improve forecasting performance while avoiding the direct sharing of data among
different institutions. Specifically, a pre-trained forecasting model is first
learned based on the data of a source mode, which can capture and memorize the
source travel patterns. Then, the demand data of the target dataset is encoded
into an individual knowledge part and a sharing knowledge part which will
extract travel patterns by individual extraction network and sharing extraction
network, respectively. The unsupervised knowledge adaptation strategy is
utilized to form the sharing features for further forecasting by making the
pre-trained network and the sharing extraction network analogous. Our findings
illustrate that unsupervised knowledge adaptation by sharing the pre-trained
model to the target mode can improve the forecasting performance without the
dependence on direct data sharing.
