Further deterioration of the already burdened traffic conditions is expected within the following years, especially in high population density urban regions. To cope with such problem, centralized and decentralized adaptive optimization techniques have already been proposed in literature; introducing inefficient performance though, due to the highly stochastic dynamics involved, scaling and/or model unavailability problems, as well as data transmission limitations. To confront such problems, L4GCAO, a novel, model-free, decentralized, adaptive optimization approach, has been developed for maximizing the system's overall performance, by calibrating the parameters of a given signal control strategy through decentralized self-learning elements (agents). This paper considers a realistic simulation scenario where the parameters of a signal control strategy applied at each network intersection are calibrated, to study the performance of L4GCAO. For comparison purposes, the thoroughly evaluated and verified centralized optimization counterpart approach of L4GCAO namely CAO - has also been adopted herein. The results of the study indicate that both CAO and L4GCAO present quite similar potential for improving the overall performance metric considered, with respect to a well-designed fixed time control strategy used as reference point.

Traffic congestion in urban road and freeway networks leads to a strong degradation of the network infrastructure and accordingly reduced throughput, which can be countered via suitable control measures and strategies. After illustrating the main reasons for infrastructure deterioration due to traffic congestion, a comprehensive overview of proposed and implemented control strategies is provided for three areas: urban road networks, freeway networks, and route guidance. Selected application results, obtained from either simulation studies or field implementations, are briefly outlined to illustrate the impact of various control actions and strategies. The paper concludes with a brief discussion of future needs in this important technical area.

This paper employs previously developed modeling, validation, and stimulation tools to address, for the first time, the realistic macroscopic simulation of a real large-scale motorway network. More specifically, the macroscopic simulator METANET, involving a second-order traffic flow model as well as network-relevant extensions, is utilized. A rigorous quantitative validation procedure is applied to individual network links, and subsequently a heuristic qualitative validation procedure is employed at a network level. The large-scale motorway network around Amsterdam, The Netherlands, is considered in this investigation. The main goal of the paper is to describe the application approach and procedures and to demonstrate the accuracy and usefulness of macroscopic modeling tools for large-scale motorway networks.