Reinforcement Learning for True Adaptive Traffic Signal Control-Reference-Cited by-同舟云学术

Reinforcement Learning for True Adaptive Traffic Signal Control

Published:2003-05 Issue:3 Volume:129 Page:278-285
ISSN:0733-947X
Container-title:Journal of Transportation Engineering
language:en
Short-container-title:J. Transp. Eng.

Author:

Abdulhai Baher¹,Pringle Rob²,Karakoulas Grigoris J.³

Affiliation:

1. Assistant Professor and Director, Intelligent Transportation Systems Centre, Dept. of Civil Engineering, Univ. of Toronto, Toronto, ON, Canada M5S 1A4.

2. PhD Candidate, Intelligent Transportation Systems Centre, Dept. of Civil Engineering, Univ. of Toronto, Toronto, ON, Canada M5S 1A4.

3. Dept. of Computer Science, Univ. of Toronto, Pratt Building LP283E, 6 King’s College, Toronto, ON, Canada M5S 1A4.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%290733-947X%282003%29129%3A3%28278%29

Reference20 articles.

1. Abdulhai B. and Ritchie S. G. (1999a). “Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network.” Transportation Research—Part C 7 261–280.

2. Abdulhai B. and Ritchie S. G. (1999b). “Towards adaptive incident detection algorithms.” Proc. 6th World Congress on Intelligent Transport Systems .

3. Data Storage in the Cerebellar Model Articulation Controller (CMAC)

4. A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)

5. Bertsekas D. P. and Tsitsiklis J. N. (1996). Neuro-dynamic programming Athena Scientific Belmont Mass.

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