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A collaborative reinforcement learning approach to urban traffic control optimization
Date
2008
Abstract
The high growth rate of vehicles per capita now poses a real challenge to efficient Urban Traffic Control (UTC). An efficient solution to UTC must be adaptive in order to deal with the highly-dynamic nature of urban traffic. In the near future, global positioning systems and vehicle-tovehicle/ infrastructure communication may provide a more detailed local view of the traffic situation that could be employed for better global UTC optimization. In this paper we describe the design of a next-generation UTC system that exploits such local knowledge about a junction’s traffic in order to optimize traffic control. Global UTC optimization is achieved using a local Adaptive Round Robin (ARR) phase switching model optimized using Collaborative Reinforcement Learning (CRL). The design employs an ARR-CRL-based agent controller for each signalized junction that collaborates with neighbouring agents in order to learn appropriate phase timing based on the traffic pattern. We compare our approach to non-adaptive fixed-time UTC system and to a saturation balancing algorithm in a largescale simulation of traffic in Dublin’s inner city centre. We show that the ARR-CRL approach can provide significant improvement resulting in up to ~57% lower average waiting time per vehicle compared to the saturation balancing algorithm.
Supervisor
Description
peer-reviewed
Publisher
IEEE Computer Society
Citation
Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology;2008
Files
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2008_Salkham.pdf
Adobe PDF, 529.27 KB
ULRR Identifiers
Funding code
Funding Information
Science Foundation Ireland (SFI)