Modeling Network-level Traffic Flow Transitions on Sparse Data

Document Type

Conference Proceeding

Publication Date

8-14-2022

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.

Identifier

85137145417 (Scopus)

ISBN

[9781450393850]

Publication Title

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

External Full Text Location

https://doi.org/10.1145/3534678.3539236

First Page

835

Last Page

845

Grant

2153311

Fund Ref

National Science Foundation

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