Modeling Network-level Traffic Flow Transitions on Sparse Data

TL;DR

This paper introduces DTIGNN, a novel method for modeling and predicting network-level traffic flow transitions in urban environments using sparse data, incorporating traffic signals and fundamental transition equations.

Contribution

The paper presents DTIGNN, a new approach that effectively models traffic flow dynamics from sparse data by integrating traffic signals and transportation principles.

Findings

DTIGNN outperforms existing methods in traffic prediction accuracy.

The approach effectively handles sparse and incomplete traffic data.

It supports better decision-making in transportation management.

Abstract

Modeling how network-level traffic flow changes in the urban environment is useful for decision-making in transportation, public safety and urban planning. The traffic flow system can be viewed as a dynamic process that transits between states (e.g., traffic volumes on each road segment) over time. In the real-world traffic system with traffic operation actions like traffic signal control or reversible lane changing, the system's state is influenced by both the historical states and the actions of traffic operations. In this paper, we consider the problem of modeling network-level traffic flow under a real-world setting, where the available data is sparse (i.e., only part of the traffic system is observed). We present DTIGNN, an approach that can predict network-level traffic flows from sparse data. DTIGNN models the traffic system as a dynamic graph influenced by traffic signals, learns the transition models grounded by fundamental transition equations from transportation, and predicts future traffic states with imputation in the process. Through comprehensive experiments, we demonstrate that our method outperforms state-of-the-art methods and can better support decision-making in transportation.