Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow

doi:10.1109/tits.2020.3028994

This content will become publicly available on October 29, 2021

Title: Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow

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Abstract

In this work, we investigated stochastic distribution control theory-based traffic signal optimization to achieve a smooth and uniform flow of vehicles through signalized intersections. In this context, the static and linear dynamic stochastic distribution models were developed to express the relationship between the signal timing and the traffic queue length together with its probability density function. Two stochastic distribution control algorithms were designed to control the signal timing at intersections such that the probability density function of the traffic queue of each intersection road segment is made as narrow and as small as possible. Also, a recursive input-output traffic queue estimation model was proposed, which is data-driven and dynamic in nature, to calculate real-time traffic queue length using traffic signal timings and loop-detector data. The control algorithms were evaluated for a one-signal corridor, two-signal corridor, and 2 x 2 network of signalized intersections. MATLAB simulation examples are provided to demonstrate the use of the proposed algorithms and comparison to the existing widely-used semi-actuated control has been made. Desired results were obtained.

Authors:

^[1];

^[2];

^[3]; Young, Stanley ^[4]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Univ. of Southern California, Los Angeles, CA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Kansas State Univ., Manhattan, KS (United States)
National Renewable Energy Laboratory (NREL)

Publication Date:: 2020-10-29

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office

OSTI Identifier:: 1706242

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Intelligent Transportation Systems

Additional Journal Information:: Journal Volume: 20; Journal Issue: 11; Journal ID: ISSN 1524-9050

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Signalized Intersections; Stochastic optimal control; Simulation

Citation Formats


                    Wang, Hong, Patil, Sagar, Aziz, H. M. Abdul, and Young, Stanley. Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow.  United States: N. p., 2020. 
        Web.  doi:10.1109/tits.2020.3028994.

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                    Wang, Hong, Patil, Sagar, Aziz, H. M. Abdul, & Young, Stanley. Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow.  United States.  doi:10.1109/tits.2020.3028994.

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                    Wang, Hong, Patil, Sagar, Aziz, H. M. Abdul, and Young, Stanley. Thu .  
        "Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow".  United States.  doi:10.1109/tits.2020.3028994.

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@article{osti_1706242,

  title        = {Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow},

  author       = {Wang, Hong and Patil, Sagar and Aziz, H. M. Abdul and Young, Stanley},

  abstractNote = {In this work, we investigated stochastic distribution control theory-based traffic signal optimization to achieve a smooth and uniform flow of vehicles through signalized intersections. In this context, the static and linear dynamic stochastic distribution models were developed to express the relationship between the signal timing and the traffic queue length together with its probability density function. Two stochastic distribution control algorithms were designed to control the signal timing at intersections such that the probability density function of the traffic queue of each intersection road segment is made as narrow and as small as possible. Also, a recursive input-output traffic queue estimation model was proposed, which is data-driven and dynamic in nature, to calculate real-time traffic queue length using traffic signal timings and loop-detector data. The control algorithms were evaluated for a one-signal corridor, two-signal corridor, and 2 x 2 network of signalized intersections. MATLAB simulation examples are provided to demonstrate the use of the proposed algorithms and comparison to the existing widely-used semi-actuated control has been made. Desired results were obtained.},

  doi          = {10.1109/tits.2020.3028994},

  journal      = {IEEE Transactions on Intelligent Transportation Systems},

  number       = 11,

  volume       = 20,

  place        = {United States},

  year         = {2020},

  month        = {10}

}

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