Latest revision as of 20:06, 28 January 2021

Abstract

eer-reviewed 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.

Original document

The different versions of the original document can be found in:

• http://hdl.handle.net/10344/2121

• https://ulir.ul.ie/bitstream/10344/2121/2/2008_Salkham.pdf

• http://xplorestaging.ieee.org/ielx5/4740404/4740584/04740684.pdf?arnumber=4740684,

http://dx.doi.org/10.1109/wiiat.2008.88

• https://ulir.ul.ie/handle/10344/2121,

http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000004740684,

https://www.cs.tcd.ie/publications/tech-reports/reports.09/TCD-CS-2009-03.pdf,

https://ieeexplore.ieee.org/document/4740684,

https://dl.acm.org/citation.cfm?id=1487108,

http://www.tara.tcd.ie/bitstream/handle/2262/32669/a+collaborative.pdf?sequence=1,

http://ieeexplore.ieee.org/document/4740684,

https://ulir.ul.ie/bitstream/10344/2121/2/2008_Salkham.pdf,

https://academic.microsoft.com/#/detail/2134698488