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Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

Author

Listed:
  • Suhaib Alshayeb

    (Department of Civil & Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, 341A Benedum Hall, 3700 O’Hara Street Pittsburgh, Pittsburgh, PA 15261, USA)

  • Aleksandar Stevanovic

    (Department of Civil & Environmental Engineering, Swanson School of Engineering, University of Pittsburgh, 218D Benedum Hall, 3700 O’Hara Street Pittsburgh, Pittsburgh, PA 15261, USA)

  • B. Brian Park

    (Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22911, USA)

Abstract

Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component of the PI is the stop penalty “ K ”, which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K -factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is utilized to develop prediction models for the K -factor. The proposed K -factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behavior, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models’ quality in capturing the individual impact of the included parameters on the K -factor. The developed models showed an excellent performance in estimating the K -factor under multiple conditions. Future research shall evaluate the findings by using field-based K -values in optimizing signals to reduce FC.

Suggested Citation

  • Suhaib Alshayeb & Aleksandar Stevanovic & B. Brian Park, 2021. "Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization," Energies, MDPI, vol. 14(21), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7431-:d:674485
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    References listed on IDEAS

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    1. Suhaib Alshayeb & Aleksandar Stevanovic & Nemanja Dobrota, 2021. "Impact of Various Operating Conditions on Simulated Emissions-Based Stop Penalty at Signalized Intersections," Sustainability, MDPI, vol. 13(18), pages 1-30, September.
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    Cited by:

    1. Suhaib Alshayeb & Aleksandar Stevanovic & Nikola Mitrovic & Elio Espino, 2022. "Traffic Signal Optimization to Improve Sustainability: A Literature Review," Energies, MDPI, vol. 15(22), pages 1-24, November.

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