Using Deep Probabilistic Reinforcement Learning to Improve Traffic Flow with Partial Vehicle Detection for Intelligent Traffic Signal Control
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2021-06-18
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en
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With the constant rise of vehicles on the streets, traffic flow needs to be
as optimal as possible. Improving traffic flow requires intelligent traffic signal
control (the field of controlling traffic lights). Mismanagement of traffic
lights can cause traffic congestion. Traffic congestion can lead to accidents,
wasted productivity, and, most importantly, is terrible for the environment
(with fossil fuel cars). Several deep reinforcement learning methods have
been suggested to improve TSC, but they all assume perfectly observed
data. However, the assumption of always being able to observe how many
cars are on each lane is not realistic. Real-world observations are inherently
noisy, sensors can fail, and some sensors might be left out as cost-saving
measures. This work, therefore, focuses on partial vehicle detection in traffic
signal control. Specifically trying to improve average travel time given
only partial observations of cars at intersections. A variational autoencoder
(VAE) has been developed using graph neural networks (GNN's) for the
encoder and decoder network to solve this problem. This VAE tries to re-
construct the real-world situation and the uncertainty of its prediction given
lane counts at a mix of observed and unobserved intersections. A state-of-
the-art reinforcement learning method called CoLight is then used to test
this VAE in the Cityflow simulator to see how well it performs. Results
show that the CoLight model performs better when using the reconstructed
modes from the VAE (515) compared to no information at all (1146) at the
unobserved intersections. The CoLight model with the additional uncertainty
measure performs marginally better (490) than the model that only
gets the reconstruction. However, the MaxPressure + FixedTime baseline
still outperforms all models using the average travel time metric (402).
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Faculteit der Sociale Wetenschappen