Abstract
ALINEA, a local feedback romp-metering strategy, has been shown to be a remarkably simple, highly efficient and easy application. This paper presents a microscopic simulation-based method to optimize the operational parameters of the algorithm, as an alternative to the difficult task of fine-tuning them In real-world testing. Four parameters, including the update cycle of the metering rate, a constant regulator, the location and the desired occupancy of the downstream detector station, are considered. A Genetic Algorithm that searches the optimal combination of parameter values Is employed. Simulation results show that the genetic algorithm is able to find a set of parameter values that can optimize the performance of the ALINEA algorithm.