Short-term traffic flow prediction model based on a shared weight gate recurrent unit neural network

Accurate traffic flow prediction is critical for enhancing traffic network operational efficiency. With the continuous expansion of traffic networks, providing reliable and efficient multi-step traffic flow prediction for large-scale traffic networks with a large number of sensors deployed has become a challenging issue. In this paper, we propose a multi-step many-to-many traffic prediction model for large-scale traffic networks, called spatio-temporal Shared GRU (STSGRU), which receive inputs from multiple sensors and provides predictions for all sensors simultaneously. First, we model the weekly pattern of traffic flow, using periodicity to explore long-term temporal features and provide smooth traffic flow to reduce the impact of data volatility. Second, different from existing models, we propose a shared weight mechanism to achieve many-to-many prediction without mapping traffic networks to images or graph structures. The proposed model strikes a delicate balance between complexity and accuracy. We validate the effectiveness of the proposed method on the Caltrans Performance Measurement System (PeMS) dataset. The results show that our model achieves similar prediction performance with advanced graph neural networks and has higher flexibility.