  As a crucial component in intelligent transportation systems, traffic flow
prediction has recently attracted widespread research interest in the field of
artificial intelligence (AI) with the increasing availability of massive
traffic mobility data. Its key challenge lies in how to integrate diverse
factors (such as temporal rules and spatial dependencies) to infer the
evolution trend of traffic flow. To address this problem, we propose a unified
neural network called Attentive Traffic Flow Machine (ATFM), which can
effectively learn the spatial-temporal feature representations of traffic flow
with an attention mechanism. In particular, our ATFM is composed of two
progressive Convolutional Long Short-Term Memory (ConvLSTM
\cite{xingjian2015convolutional}) units connected with a convolutional layer.
Specifically, the first ConvLSTM unit takes normal traffic flow features as
input and generates a hidden state at each time-step, which is further fed into
the connected convolutional layer for spatial attention map inference. The
second ConvLSTM unit aims at learning the dynamic spatial-temporal
representations from the attentionally weighted traffic flow features. Further,
we develop two deep learning frameworks based on ATFM to predict citywide
short-term/long-term traffic flow by adaptively incorporating the sequential
and periodic data as well as other external influences. Extensive experiments
on two standard benchmarks well demonstrate the superiority of the proposed
method for traffic flow prediction. Moreover, to verify the generalization of
our method, we also apply the customized framework to forecast the passenger
pickup/dropoff demands in traffic prediction and show its superior performance.
Our code and data are available at
{\color{blue}\url{https://github.com/liulingbo918/ATFM}}.
