TY - JOUR
TI - Traffic Signal Settings Optimization Using Gradient Descent
AU - Możejko, Marcin
AU - Brzeski, Maciej
AU - Mądry, Łukasz
AU - Skowronek, Łukasz
AU - Gora, Paweł
TI - Traffic Signal Settings Optimization Using Gradient Descent
AB - 
	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).
VL - 2018
IS - Volume 27
PY - 2018
SN - 1732-3916
C1 - 2083-8476
SP - 19
EP - 30
DO - 10.4467/20838476SI.18.002.10407
UR - https://ejournals.eu/en/journal/schedae-informaticae/article/traffic-signal-settings-optimization-using-gradient-descent
KW - traffic optimization
KW - metamodels
KW - activation functions
KW - genetic algorithm
KW - gradient descent