Optimal traffic signal control for alleviation of congestion based on traffic density prediction by model predictive control

Hiroaki Nakanishi, Toru Namerikawa

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

4 Citations (Scopus)

Abstract

The purpose of this paper is to propose an optimal traffic signal control in accordance with traffic density to change every moment in traffic network. We focus on calculating optimal signaling split based on traffic volume prediction by model predictive control. Therefore, we aim the achievement of alleviation of congestion in traffic network considering actual traffic situation. Finally the achievement of the purpose is evaluated by controlling of traffic signal in numerical simulations and the relationship between green time and traffic density is examined.

Original languageEnglish
Title of host publication2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1273-1278
Number of pages6
ISBN (Electronic)9784907764500
DOIs
Publication statusPublished - 2016 Nov 18
Event55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016 - Tsukuba, Japan
Duration: 2016 Sep 202016 Sep 23

Other

Other55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016
CountryJapan
CityTsukuba
Period16/9/2016/9/23

Keywords

  • Intelligent Transport system
  • Model Predictive Control
  • Traffic signal control

ASJC Scopus subject areas

  • Control and Optimization
  • Instrumentation
  • Control and Systems Engineering

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  • Cite this

    Nakanishi, H., & Namerikawa, T. (2016). Optimal traffic signal control for alleviation of congestion based on traffic density prediction by model predictive control. In 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016 (pp. 1273-1278). [7749173] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SICE.2016.7749173