Traffic Signal Control in partially observable environments using Graph Neural Networks with Variational Autoencoders.
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2021-06-18
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en
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Abstract
Tra c Signal Control (TSC) is used to improve the tra c e ciency of a
district or city, which is more important than ever with populations rising.
Current TSC methods, however, do not take the uncertainty of the real world
into account and therefore perform sub-optimal with missing or incorrect
data. This work proposes a Graph Neural Network Variational Autoencoder
architecture to reconstruct missing or incorrect data, so that these TSC
systems are still able to perform e ciently in the real world. In experiments
is demonstrated that the proposed model improves the results of the state-
of-the-art Colight model in an uncertain environment compared to the same
model without the use of reconstructed data. However, it does not perform
nearly as well as in an fully observable environment.
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Faculteit der Sociale Wetenschappen