Construction of a traffic flow prediction model based on neural ordinary differential equations and Spatiotemporal adaptive networks.

ABSTRACT: To address the issue of spatiotemporal illusion in short-term traffic flow prediction and deeply explore the underlying short-term traffic flow network characteristics, a traffic flow prediction model that combines long-term spatiotemporal heterogeneity with short-term spatiotemporal features is proposed. In the long-term spatiotemporal branch, the Transformer structure is employed, and a self-supervised masking mechanism is utilized to pretrain the heterogeneity in long-term temporal and spatial dimensions separately. Additionally, a spatiotemporal adaptive module is designed, which adapts to and guides short-term traffic flow prediction across time series and traffic flow networks. In the short-term spatiotemporal branch, a recurrent neural ordinary differential equation (ODE) module is

SUBMITTER: Ma L 

PROVIDER: S-EPMC11928730 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature