Modeling Network-level Traffic Flow Transitions on Sparse Data

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.

arXiv comment9 pages + 3 pages appendix. Accepted by SIGKDD 2022

References in corpus (5)

• T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
• Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
• GAIN: Missing Data Imputation using Generative Adversarial Nets
• Deep Graph Contrastive Representation Learning
• Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference

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