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2023

FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph

Liu, Zhanyu, Liang, Chumeng, Zheng, Guanjie et al.

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This paper proposes the fine-grained traffic prediction task (e.g.

interval between data points is 1 minute), which is essential to traffic-related downstream applications.
Under this setting, traffic flow is highly influenced by traffic signals and the correlation between traffic nodes is dynamic.
As a result, the traffic data is non-smooth between nodes, and hard to utilize previous methods which focus on smooth traffic data.

FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph · Around

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