Robust signal timing for arterials under day-to-day demand variations

Lihui Zhang, Yafeng Yin, Yingyan Lou

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


This paper formulates a scenario-based stochastic programming model to optimize the timing of pretimed signals along arterials under day-to-day demand variations or future uncertain traffic growth. Demand scenarios and their corresponding probabilities of occurrence are introduced to represent the demand uncertainty. On the basis of a cell-transmission representation of traffic dynamics, cycle length, green splits, phase sequences, and offsets are determined to minimize the expected delay incurred by high-consequence demand scenarios. A simulation-based genetic algorithm is proposed to solve the model, and a numerical example is presented to verify and validate the model.

Original languageEnglish (US)
Pages (from-to)156-166
Number of pages11
JournalTransportation Research Record
Issue number2192
StatePublished - Dec 1 2010
Externally publishedYes

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


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