Multi-View Traffic Flow Prediction Model Based on Spatiotemporal Convolution

Predicting traffic flow has always been a significant task in intelligent transportation systems. Due to the substantial temporal and spatial dependencies of traffic flow sequences, accurately predicting traffic flow poses a considerable challenge. Many existing works primarily rely on recurrent neural networks, graph neural networks, and Transformer models to establish traffic flow prediction models. To better extract features and enhance efficiency, a traffic flow prediction model based on multi-view spatiotemporal convolution (MVSC) is proposed. This model learns the representation of sequence data at the input encoding layer and incorporates location and time information. In the spatiotemporal feature representation learning layer, considering the diverse periodic patterns in sequences, several representation learning modules are designed, conducting local spatiotemporal feature exploration through one dimensional convolution and then accomplishing global spatiotemporal feature mining based on causal convolution. To further enhance the model’s utilization of spatiotemporal features, a channel attention mechanism is introduced at the prediction layer. The forecasting method employed in the study is direct multistep, and subsequent experiments conducted on two real datasets demonstrate that the MVSC model exhibits a certain degree of superiority in MAE, RMSE, and MAPE for both short-term and long-term predictions compared to existing models. And through the latest experiments and investigations, it has been found that MVSC has improved MAPE performance by about 1.2% compared to recent models such as RTGCN and STRGCN, achieving the intended outcomes.