NDLI: A Collaborative Reinforcement Learning Approach to Urban Traffic Control Optimization

Content Provider	IEEE Xplore Digital Library
Author	Salkham, A. Cunningham, R. Garg, A. Cahill, V.
Copyright Year	2008
Description	Author affiliation: Distrib. Syst. Group, Trinity Coll. Dublin, Dublin (Salkham, A.; Cunningham, R.; Garg, A.; Cahill, V.)
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-to-vehicle/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 large-scale 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.
Starting Page	560
Ending Page	566
File Size	540870
Page Count	7
File Format	PDF
ISBN	9780769534961
DOI	10.1109/WIIAT.2008.88
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Urban Traffic Control Reinforcement Learning International collaboration Collaborative Reinforcement Learning Optimization Vehicles Design optimization Learning Traffic control Communication system traffic control Timing Large-scale systems Round robin Signal design
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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