Abstract
In this paper we present the most important results of our experimentation with artificial neural networks for correcting offset relative error between two consecutive sets of traffic lights. Neural networks allow us to estimate the length of the queue of vehicles stopped in front of the stop line waiting for the red light to change to green. We will check that this length is an essential parameter for solving the offset problem. Training data and test data for the ANN are provided by a simulator specifically built up for this purpose. The performance of the simulator is tested with real data. An algorithm to improve the offset based on the queue length provided by the ANN was proposed. Finally, it was proved that its proposals provide a path to the optimal offset.
This paper is based upon data provided by the Traffic Control Department of the city of Gijón (Spain). The author would like to thank the Gijón City Council for its helpful collaboration
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López, S., Hernández, P., Hernández, A., García, M. (1999). Artificial neural networks as useful tools for the optimization of the relative offset between two consecutive sets of traffic lights. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0100547
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DOI: https://doi.org/10.1007/BFb0100547
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