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Automatic identification of near-stationary traffic states based on the PELT changepoint detection

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  • Yan, Qinglong
  • Sun, Zhe
  • Gan, Qijian
  • Jin, Wen-Long

Abstract

The existence of stationary states during peak periods has been an underlying assumption of many studies on analysis, operations, control, and management of transportation networks. In Cassidy (1998), a method was proposed to manually identify near-stationary states by visually inspecting transformed curves of cumulative total vehicle counts and occupancies. Such near-stationary states are important for calibrating fundamental diagrams, identifying active bottlenecks and incidents, and quantifying capacity drop magnitudes. To the best of our knowledge, however, there lacks an automatic method that can be applied to efficiently identify near-stationary states from a large amount of data.

Suggested Citation

  • Yan, Qinglong & Sun, Zhe & Gan, Qijian & Jin, Wen-Long, 2018. "Automatic identification of near-stationary traffic states based on the PELT changepoint detection," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 39-54.
    • Handle: RePEc:eee:transb:v:108:y:2018:i:c:p:39-54
    DOI: 10.1016/j.trb.2017.12.007
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    4. Bae, Bumjoon & Liu, Yuandong & Han, Lee D. & Bozdogan, Hamparsum, 2019. "Spatio-temporal traffic queue detection for uninterrupted flows," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 20-34.

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