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Traffic State Data Imputation: An Efficient Generating Method Based on the Graph Aggregator
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AI
DOI：10.1109/TITS.2021.3119638.png)
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摘要
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Road traffic state estimation is an essential component of intelligent transportation systems (ITSs). However, road traffic state data collected by traffic detectors are often incomplete, which can cause problems across a variety of transportation applications, such as traffic state prediction and pattern recognition. We present GA-GAN (Graph Aggregate Generative Adversarial Network), consisting of graph sample and aggregate (GraphSAGE) and a generative adversarial network (GAN), to impute missing road traffic state data. Instead of using the original road network structure, which presents the spatial information to process a graph operation, we reconstruct the road network according to the correlation coefficients of road historical data. We utilize GraphSAGE to aggregate the temporal-spatial information from the neighbors of each road in the reconstructed road network. GAN is used to generate complete traffic state data from the extracted temporal-spatial information to achieve traffic state data imputation. To illustrate the efficient performance of the model, experiments are conducted on traffic data collected from California and Seattle, Washington, showing that the proposed model outperforms state-of-the-art methods.
Keyword:
Roads
Detectors
Generative adversarial networks
Training
Correlation
Data models
Generators
Traffic data imputation
graph aggregate
generative adversarial network
deep learning
graph network
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
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