Enhancing the performance of existing urban traffic light control through extremum-seeking

Ronny Kutadinata, Will Moase, Chris Manzie, Lele Zhang, Tim Garoni

Research output: Contribution to journalArticleResearchpeer-review

19 Citations (Scopus)

Abstract

Urban traffic light controllers are responsible for maintaining good performance within the transport network. Most existing and proposed controllers have design parameters that require some degree of tuning, with the sensitivity of the performance measure to the parameter often high. To date, tuning has been largely treated as a manual calibration exercise but ignores the effects of changes in traffic condition, such as demand profile evolution due to urban population growth. To address this potential shortcoming, we seek to use a newly developed extremum-seeker to calibrate the parameters of existing urban traffic light controllers in real-time such that a certain performance measure is optimised. The results are demonstrated for three categories of traffic controllers on a microscopic urban traffic simulation. It is demonstrated that the extremum-seeking scheme is able to seek the optimal parameters, with respect to a certain performance measure, for each of these traffic light controllers in an urban, uni-modal traffic environment.
Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalTransportation Research Part C: Emerging Technologies
Volume62
DOIs
Publication statusPublished - Jan 2016

Keywords

  • Adaptive control
  • Extremum-seeking
  • Online calibration
  • Traffic control

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