Technology has rapidly changed the opportunities available to solve main problems, such as congestion and CO2 emissions. The increasing car ownership levels and population of today’s cities have created more congestion at intersections located in popular destinations, such as CBDs or stadiums, as these attract flows from origins spatially distributed across a metropolis. According to the literature, solutions, such as Adaptive Traffic Control Systems (ATCSs) and Artificial Intelligence (AI), are essential tools to improve traffic conditions. However, in these destinations, when events take place or there is an emergency, oversaturated traffic conditions may occur and it has been judged from the literature that the green timings allocated to the critical approaches from these systems may not be enough to decongestion the network. As a result, additional signal plans specified for such cases can be implemented to support and improve traffic conditions. These additional signal plans could be later fed to the algorithms of ATCSs, in order to make ATCSs applicable to all possible circumstances. In this paper, a practical application has been developed for the Olympic Stadium in Athens, for the majority of events that take place in this area. A substantial number of signal programs were designed using specified criteria to adjust the green timings needed during these events. These new signal plans have been designed by the Athens Traffic Management Centre and aim to support policy makers to plan towards sustainability, efficiency and resilience.


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    Title :

    Non – Optimized Demand Responsive Signal Plans to Support Adaptive Traffic Control Systems (ATCSs) in Oversaturated Traffic Conditions


    Additional title:

    Lecture Notes in Intelligent Transportation and Infrastructure



    Conference:

    Conference on Sustainable Urban Mobility ; 2024 ; Kalivia Filaktis, Greece September 04, 2024 - September 06, 2024



    Publication date :

    2025-03-10


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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