Abstract
Significant slowdowns in road traffic induced by increased traffic demand cause breakdowns and, consequently, congestion on roads. On urban highways, these congestion problems are most noticeable near on-ramps. To resolve traffic congestion on urban highways, it is necessary to apply new traffic control approaches like ramp metering, variable speed limit control (VSLC), etc. Today’s cooperative ramp metering algorithms adjust the metering rate for every on-ramp according to the overall traffic state on the highway and can establish additional cooperation with other traffic control subsystems. To avoid some problems of usability and effectiveness of today’s complex highway control systems, an approach based on autonomic properties (self-learning, self-adaptation, etc.) is proposed in this chapter. A new cooperative control method based on an adaptive neuro-fuzzy inference system is described. It can establish cooperation between VSLC and ramp metering. The new solution is tested using the CTMSIM macroscopic highway traffic simulator and Zagreb bypass as test model.
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Acknowledgements
The research reported in this chapter is supported by the FP7, Collaborative Project Intelligent Cooperative Sensing for Improved Traffic Efficiency, ICSI (FP7-317671), and by the EU COST action TU1102, Towards Autonomic Road Transport Support Systems. The authors wish to thank Nikola Bakarić for his valuable comments during the writing of this chapter.
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Gregurić, M., Ivanjko, E., Mandžuka, S. (2016). Learning-Based Control Algorithm for Ramp Metering. 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_12
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DOI: https://doi.org/10.1007/978-3-319-25808-9_12
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