Continuously Evolving Graph Neural Controlled Differential Equations for
  Traffic Forecasting
Jiajia Wu Ling Chen
As a crucial technique for developing a smart city, traffic forecasting has
become a popular research focus in academic and industrial communities for
decades. This task is highly challenging due to complex and dynamic
spatial-temporal dependencies in traffic networks. Existing works ignore
continuous temporal dependencies and spatial dependencies evolving over time.
In this paper, we propose Continuously Evolving Graph Neural Controlled
Differential Equations (CEGNCDE) to capture continuous temporal dependencies
and spatial dependencies over time simultaneously. Specifically, a continuously
evolving graph generator (CEGG) based on NCDE is introduced to generate the
spatial dependencies graph that continuously evolves over time from discrete
historical observations. Then, a graph neural controlled differential equations
(GNCDE) framework is introduced to capture continuous temporal dependencies and
spatial dependencies over time simultaneously. Extensive experiments
demonstrate that CEGNCDE outperforms the SOTA methods by average 2.34% relative
MAE reduction, 0.97% relative RMSE reduction, and 3.17% relative MAPE
reduction.