A Continuous-Time Optimal Control Approach to Congestion Control

Harshvardhan Uppaluru, Xun Liu, Hamid Emadi, Hossein Rastgoftar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Traffic congestion has become a nightmare to modern life in metropolitan cities. On average, a driver spending X hours a year stuck in traffic is one of most common sentences we often read regarding traffic congestion. Our aim in this arti-cle is to provide a method to control this seemingly ever-growing problem of traffic congestion. We model traffic dynamics using a continuous-time mass-flow conservation law, and apply optimal control techniques to control traffic congestion. First, we apply the mass-flow conservation law to specify traffic feasibility and present continuous-time dynamics for modeling traffic as a network problem by defining a network of interconnected roads (NOIR). The traffic congestion control is formulated as a boundary control problem and we use the concept of state-transition matrix to help with the optimization of boundary flow by solving a constrained optimal control problem using quadratic programming in MATLAB. Finally, we show that the proposed algorithm is successful by simulating on a network of interconnected roads based on the street map of Phoenix city.

Original languageEnglish (US)
Title of host publication2022 European Control Conference, ECC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1572-1577
Number of pages6
ISBN (Electronic)9783907144077
DOIs
StatePublished - 2022
Event2022 European Control Conference, ECC 2022 - London, United Kingdom
Duration: Jul 12 2022Jul 15 2022

Publication series

Name2022 European Control Conference, ECC 2022

Conference

Conference2022 European Control Conference, ECC 2022
Country/TerritoryUnited Kingdom
CityLondon
Period7/12/227/15/22

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Control and Systems Engineering
  • Control and Optimization
  • Modeling and Simulation

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