  To collectively forecast the demand for ride-sourcing services in all regions
of a city, the deep learning approaches have been applied with commendable
results. However, the local statistical differences throughout the geographical
layout of the city make the spatial stationarity assumption of the convolution
invalid, which limits the performance of CNNs on the demand forecasting task.
In this paper, we propose a novel deep learning framework called LC-ST-FCN
(locally connected spatiotemporal fully-convolutional neural network) to
address the unique challenges of the region-level demand forecasting problem
within one end-to-end architecture (E2E). We first employ the 3D convolutional
layers to fuse the spatial and temporal information existed in the input and
then feed the spatiotemporal features extracted by the 3D convolutional layers
to the subsequent 2D convolutional layers. Afterward, the prediction value of
each region is obtained by the locally connected convolutional layers which
relax the parameter sharing scheme. We evaluate the proposed model on a real
dataset from a ride-sourcing service platform (DiDiChuxing) and observe
significant improvements compared with a bunch of baseline models. Besides, we
also illustrate the effectiveness of our proposed model by visualizing how
different types of convolutional layers transform their input and capture
useful features. The visualization results show that fully convolutional
architecture enables the model to better localize the related regions. And the
locally connected layers play an important role in dealing with the local
statistical differences and activating useful regions.
