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FDTI: Fine-Grained Deep Traffic Inference with Roadnet-Enriched Graph

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14175))

Abstract

This paper proposes the fine-grained traffic prediction task (e.g. interval between data points is 1 min), 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. To address this problem, we propose Fine-grained Deep Traffic Inference, termed as FDTI. Specifically, we construct a fine-grained traffic graph based on traffic signals to model the inter-road relations. Then, a physically-interpretable dynamic mobility convolution module is proposed to capture vehicle moving dynamics controlled by the traffic signals. Furthermore, traffic flow conservation is introduced to accurately infer future volume. Extensive experiments demonstrate that our method achieves state-of-the-art performance and learned traffic dynamics with good properties. To the best of our knowledge, we are the first to conduct the city-level fine-grained traffic prediction.

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Notes

  1. 1.

    https://www.openstreetmap.org/.

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Acknowledgement

This work was sponsored by National Key Research and Development Program of China under Grant No.2022YFB3904204, National Natural Science Foundation of China under Grant No. 62102246, 62272301, and Provincial Key Research and Development Program of Zhejiang under Grant No. 2021C01034.

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Correspondence to Guanjie Zheng .

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Ethical Statement

The data used in this paper is collected from the wildly-used traffic simulator of KDDCUP2021 and does not contain any personal or sensitive data. The authors ensured that the data was collected in an ethical and legal manner. Hence, no personally identifiable information was obtained and people can not infer personal information through the data. The potential use of this work is accurate traffic prediction and better support of downstream tasks such as traffic signal control. This work is not potentially a part of policing or military work. The authors of this paper are committed to ethical principles and guidelines in conducting research and have taken measures to ensure the integrity and validity of the data. The use of the data in this study is in accordance with ethical standards and is intended to advance knowledge in the field of traffic prediction.

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Liu, Z., Liang, C., Zheng, G., Wei, H. (2023). FDTI: Fine-Grained Deep Traffic Inference with Roadnet-Enriched Graph. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14175. Springer, Cham. https://doi.org/10.1007/978-3-031-43430-3_11

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  • DOI: https://doi.org/10.1007/978-3-031-43430-3_11

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