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Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow
Abstract
In this work, we investigated stochastic distribution control theorybased 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 inputoutput traffic queue estimation model was proposed, which is datadriven and dynamic in nature, to calculate realtime traffic queue length using traffic signal timings and loopdetector data. The control algorithms were evaluated for a onesignal corridor, twosignal 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 widelyused semiactuated control has been made. Desired results were obtained.
 Authors:

 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:
 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:
 AC0500OR22725
 Resource Type:
 Accepted Manuscript
 Journal Name:
 IEEE Transactions on Intelligent Transportation Systems
 Additional Journal Information:
 Journal Volume: 20; Journal Issue: 11; Journal ID: ISSN 15249050
 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.
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.
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.
@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 theorybased 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 inputoutput traffic queue estimation model was proposed, which is datadriven and dynamic in nature, to calculate realtime traffic queue length using traffic signal timings and loopdetector data. The control algorithms were evaluated for a onesignal corridor, twosignal 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 widelyused semiactuated 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}
}