  Traffic flow prediction is an integral part of an intelligent transportation
system and thus fundamental for various traffic-related applications. Buses are
an indispensable way of moving for urban residents with fixed routes and
schedules, which leads to latent travel regularity. However, human mobility
patterns, specifically the complex relationships between bus passengers, are
deeply hidden in this fixed mobility mode. Although many models exist to
predict traffic flow, human mobility patterns have not been well explored in
this regard. To reduce this research gap and learn human mobility knowledge
from this fixed travel behaviors, we propose a multi-pattern passenger flow
prediction framework, MPGCN, based on Graph Convolutional Network (GCN).
Firstly, we construct a novel sharing-stop network to model relationships
between passengers based on bus record data. Then, we employ GCN to extract
features from the graph by learning useful topology information and introduce a
deep clustering method to recognize mobility patterns hidden in bus passengers.
Furthermore, to fully utilize Spatio-temporal information, we propose GCN2Flow
to predict passenger flow based on various mobility patterns. To the best of
our knowledge, this paper is the first work to adopt a multipattern approach to
predict the bus passenger flow from graph learning. We design a case study for
optimizing routes. Extensive experiments upon a real-world bus dataset
demonstrate that MPGCN has potential efficacy in passenger flow prediction and
route optimization.
