Hierarchal Clusters Based Traffic Control System | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 1, Volume 29, Issue 1, January 2020, Page 1-12 PDF (1.44 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2020.68928 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Fady Taher* 1; Ayman EL-SAYED 2; Ahmed Shouman1; Ahmed Elmahalawy 1
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| 1Dept. of Computer Science and Eng., Faculty of Elect., Eng., Menoufia University | ||||
| 2Computer science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt. | ||||
| Abstract | ||||
| Traffic jam is a crucial issue affecting cities around the world. They are only getting worse as the population and number of vehicles continues to increase significantly. Traffic signal controllers are considered as the most important mechanism to control the traffic, specifically at intersections, the field of Machine Learning offers more advanced techniques which can be applied to provide more flexibility and make the controllers more adaptive to the traffic state. Efficient and adaptive traffic controllers can be designed using a multi-agent reinforcement learning approach, in which, each controller is considered as an agent and is responsible for controlling traffic lights around a single junction. A major problem of reinforcement learning approach is the need for coordination between agents and exponential growth in the state-action space. This paper proposes using machine learning clustering algorithm, namely, hierarchal clustering, in order to divide the targeted network into smaller sub-networks, using real traffic data of 65 intersection of the city of Ottawa to build our simulations, the paper shows that applying the proposed methodology helped solving the curse of dimensionality problem and improved the overall network performance. | ||||
| Keywords | ||||
| Adaptive traffic signal control; Clustering; Multi-agent system; Reinforcement Learning; Simulation; Traffic controller | ||||
| References | ||||
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