Adaptive performance optimization for large-scale traffic control systems

The majority of practical Large-Scale Traffic Control Systems (LSTCSs) requires the optimization (fine-tuning) of their design parameters. A tremendous amount of human effort and time is spent for optimization of the overall LSTCS, which is usually performed by experienced personnel in the lack of an automated – well established – systematic approach. This paper, investigates the efficiency of the Adaptive Fine-Tuning algorithm, when applied for automated fine-tuning of an urban traffic LSTCS with mutually-interacting control modules, each one with its distinct design parameters. The approach of AFT is based on a recently proposed Adaptive Optimization (AO) methodology that is aiming at replacing the manually-based optimization by a fully-automated procedure and is proven – using rigorous mathematical arguments – to provide with safe and reliable, efficient and rapid optimization of general LSTCSs. Simulations results demonstrate the efficiency of the proposed approach when applied to the simultaneous fine-tuning of two mutually-interacting LSTCS control modules.ISSN:1474-667