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Real-Time Fine-Grained Freeway Traffic State Estimation Under Sparse Observation

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12457))

Abstract

Obtaining sufficient traffic state (e.g. traffic flow, density, and speed) data is critical for effective traffic operation and control. Especially for emerging advanced traffic applications, fine-grained traffic state estimation is non-trivial. With the development of advanced sensing and communication technology, connected vehicles provide unprecedented opportunities to sense traffic state and change current estimation methods. However, due to the low penetration rate of connected vehicles, traditional traffic state estimation methods do not work well under fine-grained requirements. To overcome such a problem, a probabilistic approach to estimate fine-grained traffic state of freeway under sparse observation is proposed in this paper. Specifically, we propose Residual Attention Conditional Neural Process (RA-CNP), which is an approximation of Gaussian Processes Regression (GPR) using neural network, to model spatiotemporally varying traffic states. The method can comprehensively extract both constant spatial-temporal and dynamic traffic state dependency from sparse data and have better estimation accuracy. Besides, the proposed method has less computational cost compared with traditional GPR, which makes it applicable to real-time traffic estimation applications. Extensive experiments using real-world traffic data show that the proposed method provides lower estimation error and more reliable results than other traditional traffic estimation methods under sparse observation.

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Acknowledgment

This work is supported by National Key R&D Program of China No. 2017YFB1200700. Authors thank FHWA for making the NGSIM trajectory data available.

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Authors and Affiliations

  1. School of Software and Microelectronic, Peking University, Beijing, China

    Yangxin Lin & Ping Wang

  2. Department of Civil and Environmental Engineering, University of Wisconsin, Madison, USA

    Yang Zhou

  3. Department of Transportation and Urban Engineering, Technical University of Braunschweig, Braunschweig, Germany

    Shengyue Yao

  4. School of Transportation, Southeast University, Nanjing, China

    Fan Ding

  5. National Engineering Research Center for Software Engineering, Peking University, Beijing, China

    Ping Wang

  6. Ministry of Education, Key Laboratory of High Confidence Software Technologies (PKU), Beijing, China

    Ping Wang

Authors
  1. Yangxin Lin
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  2. Yang Zhou
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  3. Shengyue Yao
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  4. Fan Ding
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  5. Ping Wang
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Corresponding author

Correspondence to Ping Wang .

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Editors and Affiliations

  1. Albert-Ludwigs-Universität, Freiburg, Germany

    Frank Hutter

  2. TU Darmstadt, Darmstadt, Germany

    Kristian Kersting

  3. Ghent University, Ghent, Belgium

    Jefrey Lijffijt

  4. Saarland University, Saarbrücken, Germany

    Isabel Valera

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Lin, Y., Zhou, Y., Yao, S., Ding, F., Wang, P. (2021). Real-Time Fine-Grained Freeway Traffic State Estimation Under Sparse Observation. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12457. Springer, Cham. https://doi.org/10.1007/978-3-030-67658-2_32

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  • DOI: https://doi.org/10.1007/978-3-030-67658-2_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67657-5

  • Online ISBN: 978-3-030-67658-2

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