NDLI: Closed Loop Optimal Adaptive Traffic Signal and Ramp Control: A Case Study on Downtown Toronto

Content Provider	IEEE Xplore Digital Library
Author	El-Tantawy, S. Rezaee, K. Abdulhai, B.
Copyright Year	2015
Description	Author affiliation: Dept. of Civil Eng., Univ. of Toronto, Toronto, ON, Canada (El-Tantawy, S.; Rezaee, K.; Abdulhai, B.)
Abstract	Traffic congestion can be alleviated by infrastructure expansions, however, improving the existing infrastructure using traffic control is more plausible due to the obvious constraints on financial resources and physical space. Independent applications of Adaptive Traffic Signal Control (ATSC) and Ramp Metering (RM) have shown strong potential to effectively alleviate urban traffic congestion by adjusting the signal timings in real-time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay at intersections or minimize travel time along freeways). This paper presents the problem formulation and the framework for addressing traffic control problem using an integrated solution combining ATSC and RM. According to that problem definition, an optimal closed-loop approach for ATSC and RM can be designed using a multi-agent reinforcement learning (MARL) approach. Authors' previous studies of MARL on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto for the morning rush hour have shown unprecedented reduction in the average intersection delay up to 39% at the network level, and travel time savings of up 2to 26% along the busiest routes in downtown Toronto. Additionally, authors' studies of MARL applied to a calibrated microsimulation model of the Gardiner Expressway in Toronto, Canada resulted in 50% reduction in total travel time compared with the base case scenario.
Starting Page	2398
Ending Page	2404
File Size	326777
Page Count	7
File Format	PDF
ISSN	21530017
e-ISBN	9781467365963
DOI	10.1109/ITSC.2015.387
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-15
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Traffic control Stochastic processes Delays Games Joints Learning (artificial intelligence) Transportation microscopic simulation Reinforcement learning adaptive traffic signal control ramp metering optimal control integrated traffic control
Content Type	Text
Resource Type	Article

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