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A collaborative reinforcement learning approach to urban traffic control
Proceedings of the Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on IEEE/WIC/ACM International Conference on Intelligent Agent Technology: IAT '08
As'ad Salkham, Raymond Cunningham, Anurag Garg, and Vinny Cahill., A collaborative reinforcement learning approach to urban traffic control, Proceedings of the Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on, IEEE/WIC/ACM International Conference on Intelligent Agent Technology: IAT '08, Sydney, NSW, 9-12 Dec, 2, IEEE Computer Society, 2008, 560-566
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
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