  With the rapid development of location based services, multimodal
spatio-temporal (ST) data including trajectories, transportation modes, traffic
flow and social check-ins are being collected for deep learning based methods.
These deep learning based methods learn ST correlations to support the
downstream tasks in the fields such as smart mobility, smart city and other
intelligent transportation systems. Despite their effectiveness, ST data fusion
and forecasting methods face practical challenges in real-world scenarios.
First, forecasting performance for ST data-insufficient area is inferior,
making it necessary to transfer meta knowledge from heterogeneous area to
enhance the sparse representations. Second, it is nontrivial to accurately
forecast in multi-transportation-mode scenarios due to the fine-grained ST
features of similar transportation modes, making it necessary to distinguish
and measure the ST correlations to alleviate the influence caused by entangled
ST features. At last, partial data modalities (e.g., transportation mode) are
lost due to privacy or technical issues in certain scenarios, making it
necessary to effectively fuse the multimodal sparse ST features and enrich the
ST representations. To tackle these challenges, our research work aim to
develop effective fusion and forecasting methods for multimodal ST data in
smart mobility scenario. In this paper, we will introduce our recent works that
investigates the challenges in terms of various real-world applications and
establish the open challenges in this field for future work.
