A fully-autonomic methodology for embedding self-tuning competence in online traffic control systems
Abstract
Recent advances in technology and computer sci- ence play a key role towards the design of the next generation of Intelligent Transportation Systems (ITS). The architecture of such complex systems is crucial to include supporting algorithms that can embody self managing properties within the existing ITS strategies. This paper presents a recently developed adaptive optimization algorithm that combines methodologies from the fields of traffic engineering, optimization and machine learning in order to embed self-tuning properties in traffic control systems. The derived Adaptive Fine-Tuning (AFT) algorithm comprises a fully-autonomic tool that can be used in online ITS applications of various types, in order to optimize their performance by automatically fine-tuning the system’s design parameters. The algorithm is evaluated in simulation experiments, examining the ability of self-tuning the design pa- rameters of a traffic control strategy for urban road networks. Show more
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https://doi.org/10.3929/ethz-b-000276243Publication status
publishedPublisher
ETH ZurichEvent
Organisational unit
08686 - Gruppe Strassenverkehrstechnik
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
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Is previous version of: http://hdl.handle.net/20.500.11850/275874
Notes
1st International Systems Competition on Autonomic Features and Technologies for Road Traffic Flow Modelling and Control Systems, Demo Session in ITSC 2013.More
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