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2015 IEEE International Conference on Autonomic Computing (ICAC) (2015)
Grenoble, France
July 7, 2015 to July 10, 2015
ISBN: 978-1-4673-6970-1
pp: 261-266
Traffic experts try to optimise the signalisation of traffic light controllers during design-time based on historic traffic flow data. Traffic exhibits dynamic behaviour. Due to changing traffic demands, new and flexible traffic management systems are needed that optimise themselves during runtime. Organic Traffic Control is such a decentralised, self-organising system that adapts the green times of traffic ights to the current traffic conditions. Forecasts of future traffic conditions may result in a faster adaptation, higher robustness and flexibility. The combination of several forecasting techniques leads to fewer forecast errors. This paper presents three novel combination strategies from the machine learning domain using an Artificial Neural Network, Historic Load Curves and an Extended Classifier System.
Artificial neural networks, Predictive models, Standards, Neurons, Forecasting, Runtime, Robustness

M. Sommer, S. Tomforde and J. Hahner, "Learning a Dynamic Re-combination Strategy of Forecast Techniques at Runtime," 2015 IEEE International Conference on Autonomic Computing (ICAC), Grenoble, France, 2015, pp. 261-266.
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