Abstract
With the advent of autonomous vehicles, the field of traffic intersection management has changed. Most of the current methods for intersection management use either stochastic methods for optimizing single scheduling scenarios or deterministic algorithms to optimize parameters for intersection traffic lights. This paper proposes and explores the application of multi-objective evolutionary algorithm (MOEA) to manage a traffic intersection in real time. To achieve this goal, this work implements an intersection manager (IM) that divides the continuous problem into smaller discrete time steps. The vehicular behaviour in single time steps is then optimized, considering several optimization objectives with different goals in terms of overall performance.
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Ripon, K.S.N., Dissen, H., Solaas, J. (2016). Real Time Traffic Intersection Management Using Multi-objective Evolutionary Algorithm. In: Martín-Vide, C., Mizuki, T., Vega-Rodríguez, M. (eds) Theory and Practice of Natural Computing. TPNC 2016. Lecture Notes in Computer Science(), vol 10071. Springer, Cham. https://doi.org/10.1007/978-3-319-49001-4_9
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DOI: https://doi.org/10.1007/978-3-319-49001-4_9
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