  Traffic flow prediction is crucial for urban traffic management and public
safety. Its key challenges lie in how to adaptively integrate the various
factors that affect the flow changes. In this paper, we propose a unified
neural network module to address this problem, called Attentive Crowd Flow
Machine~(ACFM), which is able to infer the evolution of the crowd flow by
learning dynamic representations of temporally-varying data with an attention
mechanism. Specifically, the ACFM is composed of two progressive ConvLSTM units
connected with a convolutional layer for spatial weight prediction. The first
LSTM takes the sequential flow density representation as input and generates a
hidden state at each time-step for attention map inference, while the second
LSTM aims at learning the effective spatial-temporal feature expression from
attentionally weighted crowd flow features. Based on the ACFM, we further build
a deep architecture with the application to citywide crowd flow prediction,
which naturally incorporates the sequential and periodic data as well as other
external influences. Extensive experiments on two standard benchmarks (i.e.,
crowd flow in Beijing and New York City) show that the proposed method achieves
significant improvements over the state-of-the-art methods.
