Abstract
Recent advances in technology, control and computer science play a key role towards the design and deployment of the next generation of intelligent transportation systems (ITS). The architecture of such complex systems is crucial to include supporting algorithms that can embody autonomic properties within the existing ITS strategies. This chapter presents a recently developed adaptive optimization algorithm that combines methodologies from the fields of traffic engineering, automatic control, optimization and machine learning in order to embed self-tuning properties in traffic control systems. The derived adaptive fine-tuning (AFT) algorithm comprises an 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 has been evaluated in simulation experiments, examining its ability and efficiency to fine-tune in real time the design parameters of a number of traffic control systems, including signal control for urban road networks. Field results are in progress for the urban network of Chania, Greece, as well as for energy-efficient building control. Some promising preliminary field results for the traffic control problem of Chania are presented here.
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Acknowledgements
The research leading to these results has been partially funded by the European Commission FP7-ICT-5-3.5, Engineering of Networked Monitoring and Control Systems, under the contract #257806 AGILE.
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Kouvelas, A., Manolis, D., Kosmatopoulos, E., Papamichail, I., Papageorgiou, M. (2016). An Autonomic Methodology for Embedding Self-tuning Competence in Online Traffic Control Systems. In: McCluskey, T., Kotsialos, A., Müller, J., Klügl, F., Rana, O., Schumann, R. (eds) Autonomic Road Transport Support Systems. Autonomic Systems. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-25808-9_13
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DOI: https://doi.org/10.1007/978-3-319-25808-9_13
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