  Accurate traffic flow prediction is crucial for optimizing traffic
management, enhancing road safety, and reducing environmental impacts. Existing
models face challenges with long sequence data, requiring substantial memory
and computational resources, and often suffer from slow inference times due to
the lack of a unified summary state. This paper introduces ST-MambaSync, an
innovative traffic flow prediction model that combines transformer technology
with the ST-Mamba block, representing a significant advancement in the field.
We are the pioneers in employing the Mamba mechanism which is an attention
mechanism integrated with ResNet within a transformer framework, which
significantly enhances the model's explainability and performance. ST-MambaSync
effectively addresses key challenges such as data length and computational
efficiency, setting new benchmarks for accuracy and processing speed through
comprehensive comparative analysis. This development has significant
implications for urban planning and real-time traffic management, establishing
a new standard in traffic flow prediction technology.
