Traffic Signal Control Based on Reinforcement Learning with Graph Convolutional Neural Nets
| Autor: | Tomoki Nishi, Keisuke Otaki, Takayoshi Yoshimura, Keiichiro Hayakawa |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
050210 logistics & transportation
Artificial neural network business.industry Computer science Feature vector 05 social sciences 02 engineering and technology Convolutional neural network Traffic signal Traffic congestion 0502 economics and business 0202 electrical engineering electronic engineering information engineering Reinforcement learning Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ITSC |
| DOI: | 10.1109/itsc.2018.8569301 |
| Popis: | Traffic signal control can mitigate traffic congestion and reduce travel time. A model-free reinforcement learning (RL) approach is a powerful framework for learning a responsive traffic control policy for short-term traffic demand changes without prior environmental knowledge. Previous RL approaches could handle high-dimensional feature space using a standard neural network, e.g., a convolutional neural network; however, to control traffic on a road network with multiple intersections, the geometric features between roads had to be created manually. Rather than using manually crafted geometric features, we developed an RL-based traffic signal control method that employs a graph convolutional neural network (GCNN). GCNNs can automatically extract features considering the traffic features between distant roads by stacking multiple neural network layers. We numerically evaluated the proposed method in a six-intersection environment. The results demonstrate that the proposed method can find comparable policies twice as fast as the conventional RL method with a neural network and can adapt to more extensive traffic demand changes. |
| Databáze: | OpenAIRE |
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