Traffic Signal Settings Optimization Using Gradient Descent

• Autorzy: Możejko Marcin , Brzeski Maciej , Mądry Łukasz , Skowronek Łukasz , Gora Paweł

Identyfikatory

DOI

10.4467/20838476SI.18.002.10407

• Języki publikacji: EN

Abstrakty

EN

We investigate performance of a gradient descent optimization (GR) applied to the traffic signal setting problem and compare it to genetic algorithms. We used neural networks as metamodels evaluating quality of signal settings and discovered that both optimization methods produce similar results, e.g., in both cases the accuracy of neural networks close to local optima depends on an activation function (e.g., TANH activation makes optimization process converge to different minima than ReLU activation).

Słowa kluczowe

EN

traffic optimization; metamodel; activation functions; genetic algorithm; gradient descent

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2018
• Tom: Vol. 27
• Strony: 19--30
• Opis fizyczny: Bibliogr. 26 poz., rys.

Twórcy

autor

Możejko Marcin

• TensorCell

autor

Brzeski Maciej

• TensorCell
• Faculty of Mathematics and Computer Science, Jagiellonian University

autor

Mądry Łukasz

• TensorCell
• Faculty of Mathematics, Informatics and Mechanics, University of Warsaw

autor

Skowronek Łukasz

• TensorCell

autor

Gora Paweł

• TensorCell
• Faculty of Mathematics, Informatics and Mechanics, University of Warsaw

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).