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Traffic predictive control from low-rank structure

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  • Coogan, Samuel
  • Flores, Christopher
  • Varaiya, Pravin

Abstract

The operation of most signalized intersections is governed by predefined timing plans that are applied during specified times of the day. These plans are designed to accommodate average conditions and are unable to respond to large deviations in traffic flow. We propose a control approach that adjusts time-of-day signaling plans based on a prediction of future traffic flow. The prediction algorithm identifies correlated, low rank structure in historical measurement data and predicts future traffic flow from real-time measurements by determining which structural trends are prominent in the measurements. From this prediction, the controller then determines the optimal time of day to apply new timing plans. We demonstrate the potential benefits of this approach using eight months of high resolution data collected at an intersection in Beaufort, South Carolina.

Suggested Citation

  • Coogan, Samuel & Flores, Christopher & Varaiya, Pravin, 2017. "Traffic predictive control from low-rank structure," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 1-22.
    • Handle: RePEc:eee:transb:v:97:y:2017:i:c:p:1-22
    DOI: 10.1016/j.trb.2016.11.013
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    References listed on IDEAS

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    Cited by:

    1. Coogan, Samuel & Dutreix, Maxence, 2017. "Traffic Predictive Control: Case Study and Evaluation," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0bs645m2, Institute of Transportation Studies, UC Berkeley.
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    4. Osipenko, Maria, 2021. "Directional assessment of traffic flow extremes," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 353-369.

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