Autonomous self-regulating intersections in large-scale urban traffic networks: a Chania city case studyAutonomous self-regulating intersections in large-scale urban traffic networks: a Chania city case study Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2019-09-052018enFurther 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.http://creativecommons.org/licenses/by/4.0/853-8585th International Conference on Control, Decision and Information Technologies Michailidis Iakovos T. Manolis Diamantis Μανωλης Διαμαντης Michailidis Panagiotis Diakaki Christina Διακακη Χριστινα Kosmatopoulos Ilias Κοσματοπουλος Ηλιας Institute of Electrical and Electronics Engineers Population statistics Stochastic models Stochastic systems