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Traffic Incident Detection from Massive Multivariate Time-Series Data

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Cognitive Computing – ICCC 2020 (ICCC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12408))

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Abstract

Automatic incident detection (AID) aims to programatically detect vehicle traffic incidents from real-time traffic data, and improvements in AID are vital for future technology such as smart-city traffic planning. In this research we use a simple machine learning model (AdaBoost) applied to freely available traffic data from the Caltrans Performance Measurement System (PeMS) to develop a state of the art AID mechanism. In addition we discuss related work in AID to date, introduce and explore the data we use in our experiments, present our methods and results, and discuss conclusions and future work.

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Authors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, USA

    Nicholas Sterling & John A. Miller

Authors
  1. Nicholas Sterling
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  2. John A. Miller
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Corresponding author

Correspondence to Nicholas Sterling .

Editor information

Editors and Affiliations

  1. Tsinghua Shenzhen International Graduate School, Shenzhen, China

    Yujiu Yang

  2. IBM Research – Thomas J. Watson Research, New York, NY, USA

    Lei Yu

  3. Kingdee International Software Group Co., Ltd, Shenzhen, China

    Liang-Jie Zhang

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Sterling, N., Miller, J.A. (2020). Traffic Incident Detection from Massive Multivariate Time-Series Data. In: Yang, Y., Yu, L., Zhang, LJ. (eds) Cognitive Computing – ICCC 2020. ICCC 2020. Lecture Notes in Computer Science(), vol 12408. Springer, Cham. https://doi.org/10.1007/978-3-030-59585-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-59585-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59584-5

  • Online ISBN: 978-3-030-59585-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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