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Robust Signal Timing for Arterials Under Day-to-Day Demand Variations Zhang, Lihui ; Yin, Yafeng ; Lou, Yingyan

By: Contributor(s): Series: Transportation Research Record: Journal of the Transportation Research Board ; 2192Publication details: Washington DC Transportation Research Board, 2010Description: s. 156-166ISBN:
  • 9780309160674
Subject(s): Bibl.nr: VTI P8167:2192Location: TRBAbstract: 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.
Item type: Reports, conferences, monographs
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Statens väg- och transportforskningsinstitut Available

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.