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

Hong Wang, Sagar Patil, H. M. Aziz, Stanley Young

Research output: Contribution to journalArticlepeer-review

10 Scopus Citations


This work 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 $2 \times 2$ 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.

Original languageAmerican English
Pages (from-to)1885-1898
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number3
StatePublished - 1 Mar 2022

Bibliographical note

Publisher Copyright:
© 2000-2011 IEEE.

NREL Publication Number

  • NREL/JA-5400-82745


  • stochastic distribution control
  • traffic queue control
  • Traffic queue model


Dive into the research topics of 'Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow'. Together they form a unique fingerprint.

Cite this