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Spatial Network-Wide Traffic Flow Imputation With Graph Neural Network
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  • Sina Sabzekar ,
  • Rezvan Bahmani ,
  • Masoud Ghasemi ,
  • Zahra Amini
Sina Sabzekar
Sharif University of Technology

Corresponding Author:[email protected]

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Rezvan Bahmani
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Masoud Ghasemi
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Zahra Amini
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Traffic data plays an essential role in Intelligent Transportation Systems (ITS) and offers numerous advantages, including efficient traffic control and system performance improvement. However, due to the scarcity of data collection systems, missing data in traffic datasets is inevitable. Therefore, traffic data imputation becomes an essential task. Graph Neural Network (GNN) is a type of neural network that operates on graph-structured data and has shown potential in handling traffic network related tasks such as traffic prediction and traffic data imputation. In this paper, we contribute to the body of knowledge with two aspects. First, we focus on traffic data imputation using solely spatial information. Most of the studies in the literature address spatio-temporal traffic data imputation, which is a distinct task from our research. Second, Since most GNN models operates on node features, we propose an approach to construct node features for nodes in traffic networks, by leveraging available link flows. To investigate the effectiveness of the proposed method, we implement two missing scenarios, random missing (RM) and block missing (BM). We evaluate the performance of the proposed method on three different sized real-world networks: Sioux Falls, Anaheim, and Chicago. The evaluation results demonstrate that GNN models outperform other baselines for most of the missing patterns.