  Accurate traffic flow prediction, a hotspot for intelligent transportation
research, is the prerequisite for mastering traffic and making travel plans.
The speed of traffic flow can be affected by roads condition, weather,
holidays, etc. Furthermore, the sensors to catch the information about traffic
flow will be interfered with by environmental factors such as illumination,
collection time, occlusion, etc. Therefore, the traffic flow in the practical
transportation system is complicated, uncertain, and challenging to predict
accurately. This paper proposes a deep encoder-decoder prediction framework
based on variational Bayesian inference. A Bayesian neural network is
constructed by combining variational inference with gated recurrent units (GRU)
and used as the deep neural network unit of the encoder-decoder framework to
mine the intrinsic dynamics of traffic flow. Then, the variational inference is
introduced into the multi-head attention mechanism to avoid noise-induced
deterioration of prediction accuracy. The proposed model achieves superior
prediction performance on the Guangzhou urban traffic flow dataset over the
benchmarks, particularly when the long-term prediction.
