  Recently, practical applications for passenger flow prediction have brought
many benefits to urban transportation development. With the development of
urbanization, a real-world demand from transportation managers is to construct
a new metro station in one city area that never planned before. Authorities are
interested in the picture of the future volume of commuters before constructing
a new station, and estimate how would it affect other areas. In this paper,
this specific problem is termed as potential passenger flow (PPF) prediction,
which is a novel and important study connected with urban computing and
intelligent transportation systems. For example, an accurate PPF predictor can
provide invaluable knowledge to designers, such as the advice of station scales
and influences on other areas, etc. To address this problem, we propose a
multi-view localized correlation learning method. The core idea of our strategy
is to learn the passenger flow correlations between the target areas and their
localized areas with adaptive-weight. To improve the prediction accuracy, other
domain knowledge is involved via a multi-view learning process. We conduct
intensive experiments to evaluate the effectiveness of our method with
real-world official transportation datasets. The results demonstrate that our
method can achieve excellent performance compared with other available
baselines. Besides, our method can provide an effective solution to the
cold-start problem in the recommender system as well, which proved by its
outperformed experimental results.
