  Potential crowd flow prediction for new planned transportation sites is a
fundamental task for urban planners and administrators. Intuitively, the
potential crowd flow of the new coming site can be implied by exploring the
nearby sites. However, the transportation modes of nearby sites (e.g. bus
stations, bicycle stations) might be different from the target site (e.g.
subway station), which results in severe data scarcity issues. To this end, we
propose a data driven approach, named MOHER, to predict the potential crowd
flow in a certain mode for a new planned site. Specifically, we first identify
the neighbor regions of the target site by examining the geographical proximity
as well as the urban function similarity. Then, to aggregate these
heterogeneous relations, we devise a cross-mode relational GCN, a novel
relation-specific transformation model, which can learn not only the
correlations but also the differences between different transportation modes.
Afterward, we design an aggregator for inductive potential flow representation.
Finally, an LTSM module is used for sequential flow prediction. Extensive
experiments on real-world data sets demonstrate the superiority of the MOHER
framework compared with the state-of-the-art algorithms.
