This invention provides a
traffic flow prediction method and
system based on multi-perspective spatiotemporal modeling, belonging to the field of traffic
information management technology. This invention decouples the spatiotemporal modeling process into two complementary perspectives: implicit spatiotemporal modeling and globally explicit spatiotemporal relationships. Based on a selective state-
space model architecture, it performs in-depth analysis of input features from both channel and spatial location perspectives, thereby constructing a multi-perspective temporal understanding path. Furthermore, since the implicit and globally explicit temporal modeling capture corresponding
temporal information from the channel and spatial location perspectives respectively, a channel space attention module is introduced to effectively filter out unimportant
temporal information interference, further improving the overall model's
traffic flow prediction capability. The method described in this invention not only effectively improves the accuracy of
traffic flow prediction, but its design concept can also provide valuable insights for other spatiotemporal data-driven downstream tasks (such as
weather forecasting).