Temporal Super-Resolution Traffic Flow Forecasting via Continuous-Time Network Dynamics

Abstract

Traffic flow forecasting is a critical task for Intelligent Transportation Systems. However, the existed forecasting can only be conducted at certain timestamps, because the data, is discretely collected at these timestamps. In contrast, traffic flow evolves in real-time in a continuous manner in the real world. Therefore, an ideal forecasting paradigm should be performed at arbitrary timestamps instead of only at these certain timestamps. Considering the forecasting timestamps will no longer be restricted by these timestamps, we call such a paradigm as temporal super-resolution forecasting. In this paper, we incorporate the idea of neural ordinary differential equations (Neural ODEs) to handle the problem, modeling the change rate of traffic flow on the urban road. Therefore, due to the continuous nature of ordinary differential equations, the traffic flow at arbitrary timestamps can be forecasted by performing a definite integral for the change rate. The urban road is usually regarded as a network, and the change rate of which can be described by continuous-time network dynamics, we parameterize the network dynamics of the traffic flow to quantify the change rate. On these foundations, we propose Spatial-Temporal Continuous Dynamics Network (STCDN) to complete the temporal super-resolution forecasting task. Extensive experiments on public traffic flow datasets illustrate that our model can achieve high accuracy on temporal super-resolution forecasting while ensuring its performance in conventional experimental settings at these certain timestamps.