Simulation-based robust optimization for signal timing and setting.
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2009-12-30
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Alternative Title:Final report to the center for multimodal solutions for congestion mitigation;(CMS);
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Edition:Final report; Mar. 2008-Aug. 2009.
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Abstract:The performance of signal timing plans obtained from traditional approaches for
pre-timed (fixed-time or actuated) control systems is often unstable under fluctuating traffic
conditions. This report develops a general approach for optimizing the timing of pre-timed
signals along arterials under day-to-day demand variations or uncertain traffic future growth.
Based on a cell-transmission representation of traffic dynamics, a stochastic programming model
is formulated to determine cycle length, green splits, phase sequences and offsets to minimize the
expected delay incurred by high-consequence scenarios of traffic demand.
The stochastic programming model is simple in structure but contains a large number of
binary variables. Existing algorithms, such as branch and bound, are not able to solve it
efficiently, particularly when the optimization horizon is long and the network size is large.
Consequently, a simulation-based genetic algorithm is developed to solve the model. The model
and algorithm are validated and verified in two networks. It is demonstrated that the resulting
robust timing plans perform better against high-consequence scenarios without losing optimality
in the average sense. More specifically, the plans reduce substantially the mean excess delay
across the high-consequence scenarios without compromising the average delay across all
scenarios under both congested and uncongested traffic conditions.
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