Leveraging Heterogeneous Experts with Advantageous Pattern Memory Learning for Traffic Prediction

Accurate traffic prediction is essential for mitigating congestion and enabling convenient trip arrangements. However, a single modeling approach often struggles to excel across diverse traffic patterns due to the inherent complexities and external influences in traffic scenarios. To address these issues, we propose a method named Memory-enhanced Heterogeneous Mixture of Experts (MH-MoE), which leverages memory-enhanced gating to integrate multiple pretrained models. The proposed method first obtains spatio-temporal embeddings from historical traffic sequences, followed by a traffic pattern extractor to capture representative patterns. Furthermore, a memory gating module memorizes each expert's advantageous patterns and learns to allocate traffic patterns to suitable experts. Finally, by combining predictions from these experts, MH-MoE effectively leverages the strengths of heterogeneous modeling to excel across traffic patterns. Experiments on multiple traffic datasets demonstrate that MH-MoE outperforms existing methods by leveraging diverse expert strengths, improving predictive accuracy, and offering scalability and efficiency for complex traffic prediction tasks.