Research on traffic flow prediction based on temporal convolution multi-attention network

The precise and real-time forecasting of traffic is very important for the planning, control, and orientation of traffic in cities. However, forecasting circulation flows remain a troublesome issue because of the highly non-linear and complex nature of circulation systems. This research proposes a new temporal convolutional multi-attentive network-based traffic flow prediction model (TCMAN). TCMAN model captures the spatiotemporal features of traffic flow through temporal convolutional network (TCN) and codec. The codec includes several spatial-temporal attention blocks to simulate the effects of space-time factors on circulation conditions. The input traffic flow features are coded by the encoder and the output sequence is anticipated by the decoder. Finally, many experiments are carried out on the traffic datasets. Compared with the baseline method, TCMAN model has better prediction performance.