Solving Traffic Signal Scheduling Problems in Heterogeneous Traffic Network by Using Meta-Heuristics

Kaizhou Gao, Yicheng Zhang, Rong Su, Fajun Yang, Ponnuthurai Nagaratnam Suganthan, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This paper addresses a traffic signal scheduling (TSS) problem in a heterogeneous traffic network with signalized and non-signalized intersections. The objective is to minimize the total network-wise delay time of all vehicles within a given finite-time window. First, a novel model is proposed to describe a heterogeneous traffic network with signalized and non-signalized intersections. Second, five meta-heuristics are implemented to solve the TSS problem. Based on the problem characteristics, three local search operators and their ensemble are proposed. Then, five meta-heuristics with such an ensemble are proposed to solve the TSS problem. Third, experiments are carried out based on the real traffic data in the Jurong area of Singapore. The performance of the ensemble of local search operators is verified. Ten algorithms, including five meta-heuristics with and without the ensemble, are evaluated by solving 18 cases with different scales. Finally, the algorithm with the best performance is compared against the currently used traffic signal control strategies. The comparisons and discussions show the competitiveness of the proposed model and meta-heuristics.

Original languageEnglish (US)
Article number8527673
Pages (from-to)3272-3282
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Volume20
Issue number9
DOIs
StatePublished - Sep 2019

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Keywords

  • Jaya
  • Traffic signal scheduling
  • artificial bee colony
  • genetic algorithm
  • harmony search
  • water cycle algorithm

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