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Potential crowd flow prediction for new planned transportation sites is afundamental task for urban planners and administrators. Intuitively, thepotential crowd flow of the new coming site can be implied by exploring thenearby sites. However, the transportation modes of nearby sites (e.g. busstations, bicycle stations) might be different from the target site (e.g.subway station), which results in severe data scarcity issues. To this end, wepropose a data driven approach, named MOHER, to predict the potential crowdflow in a certain mode for a new planned site. Specifically, we first identifythe neighbor regions of the target site by examining the geographical proximityas well as the urban function similarity. Then, to aggregate theseheterogeneous relations, we devise a cross-mode relational GCN, a novelrelation-specific transformation model, which can learn not only thecorrelations 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. Extensiveexperiments on real-world data sets demonstrate the superiority of the MOHERframework compared with the state-of-the-art algorithms.

Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction