Traffic Signal Settings Optimization Using Gradient Descent
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Publication date: 2018
Schedae Informaticae, 2018, Volume 27, pp. 19 - 30
https://doi.org/10.4467/20838476SI.18.002.10407Authors
Traffic Signal Settings Optimization Using Gradient Descent
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).
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Information: Schedae Informaticae, 2018, Volume 27, pp. 19 - 30
Article type: Original article
Titles:
Traffic Signal Settings Optimization Using Gradient Descent
Traffic Signal Settings Optimization Using Gradient Descent
TensorCell
TensorCell
Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
TensorCell
Published at: 2018
Article status: Open
Licence: CC BY-NC-ND
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