  With the expansion of high-speed railway network and growth of passenger
transportation demands, the fleet size of electric multiple unit (EMU) in China
needs to be adjusted accordingly. Generally, an EMU train costs tens of
millions of dollars which constitutes a significant portion of capital
investment. Thus, the prediction of EMU fleet size has attracted increasing
attention from associated railway departments. First, this paper introduces a
typical architecture of convolutional neural network (CNN) and its basic
theory. Then, some data of nine indices, such as passenger traffic volume and
length of high-speed railways in operation, is collected and preprocessed.
Next, a CNN and a backpropagation neural network (BPNN) are constructed and
trained aiming to predict EMU fleet size in the following years. The
differences and performances of these two networks in computation experiments
are analyzed in-depth. The results indicate that the CNN is superior to the
BPNN both in generalization ability and fitting accuracy, and CNN can serve as
an aid in EMU fleet size prediction.
