NDLI: Multi-objective traffic light control system based on Bayesian probability interpretation

Content Provider	IEEE Xplore Digital Library
Author	Khamis, M.A. Gomaa, W. El-Shishiny, H.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science and Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt (Khamis, M.A.; Gomaa, W.) \|\| IBM Center for Advanced Studies in Cairo, Giza, Egypt (El-Shishiny, H.)
Abstract	Traffic light control is a challenging problem in modern societies. This is due to the huge number of vehicles and the high dynamics of the traffic system. Poor traffic management causes a high rate of accidents, time losses, and negative impact on the economy as well as the environment. In this paper, we develop a multiagent traffic light control system based on a multi-objective sequential decision making framework. In order to respond effectively to the changing environment and the non-stationarity of the road network, the proposed traffic light controller is based on the Bayesian interpretation of probability. We use the open source Green Light District (GLD) vehicle traffic simulator as a testbed framework. The change in road conditions is modeled by varying the vehicles generation probability distributions and adapting the Intelligent Driver Model (IDM) parameters to the adverse weather conditions. We have added a set of new performance indices in GLD based on collaborative learning to better evaluate the performance of the proposed multi-objective traffic light controller. The results show that the proposed multi-objective controller outperforms the single-objective controller.
Starting Page	995
Ending Page	1000
File Size	320487
Page Count	6
File Format	PDF
ISBN	9781467330640
ISSN	21530009
e-ISBN	9781467330633
e-ISBN	9781467330626
DOI	10.1109/ITSC.2012.6338853
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Meteorology Bayesian methods Junctions Acceleration Roads Green products Adverse weather conditions Multi-objective traffic light controller Multiagent reinforcement learning system Intelligent Driver Model Bayesian probability interpretation
Content Type	Text
Resource Type	Article

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