Integrating Multi-Graph Convolutional Networks and Temporal-Aware Multi-Head Attention for Lane-Level Traffic Flow Prediction in Urban Networks

The urban signalized road network, characterized by its dynamic and complex nature due to frequent signal control adjustments and unpredictable demand fluctuations, presents significant challenges for predicting lane-level traffic flow. This study introduces the innovative MGCN-TAMA model, which addresses these challenges by integrating multi-graph convolutional networks with a temporal-aware multi-head attention mechanism. The proposed model employs three types of adjacency matrices-a geographical matrix, a signal matrix, and an attention matrix-to capture the complex spatial dependencies among various traffic approaches. Additionally, the model utilizes temporal-aware multi-head attention to discern the nonlinear correlations in traffic variations over time. Tested on a real-world dataset from Tongxiang City, the MGCN-TAMA model significantly outperforms traditional models. Notably, in the first 30-minute prediction interval, our model achieves the lowest Mean Absolute Error, with 2.5649 vehicles per 5-minute span. These results underscore the effectiveness of combining graph-based methods with advanced attention mechanisms to enhance the accuracy of predicting lane-level traffic volumes in urban networks.