  Taxi demand prediction has recently attracted increasing research interest
due to its huge potential application in large-scale intelligent transportation
systems. However, most of the previous methods only considered the taxi demand
prediction in origin regions, but neglected the modeling of the specific
situation of the destination passengers. We believe it is suboptimal to
preallocate the taxi into each region based solely on the taxi origin demand.
In this paper, we present a challenging and worth-exploring task, called taxi
origin-destination demand prediction, which aims at predicting the taxi demand
between all region pairs in a future time interval. Its main challenges come
from how to effectively capture the diverse contextual information to learn the
demand patterns. We address this problem with a novel Contextualized
Spatial-Temporal Network (CSTN), which consists of three components for the
modeling of local spatial context (LSC), temporal evolution context (TEC) and
global correlation context (GCC) respectively. Firstly, an LSC module utilizes
two convolution neural networks to learn the local spatial dependencies of taxi
demand respectively from the origin view and the destination view. Secondly, a
TEC module incorporates both the local spatial features of taxi demand and the
meteorological information to a Convolutional Long Short-term Memory Network
(ConvLSTM) for the analysis of taxi demand evolution. Finally, a GCC module is
applied to model the correlation between all regions by computing a global
correlation feature as a weighted sum of all regional features, with the
weights being calculated as the similarity between the corresponding region
pairs. Extensive experiments and evaluations on a large-scale dataset well
demonstrate the superiority of our CSTN over other compared methods for taxi
origin-destination demand prediction.
