Accounting for dynamic speed limit control in a stochastic traffic environment: A reinforcement learning approach-Reference-Cited by-同舟云学术

Accounting for dynamic speed limit control in a stochastic traffic environment: A reinforcement learning approach

Published:2014-04 Issue: Volume:41 Page:30-47
ISSN:0968-090X
Container-title:Transportation Research Part C: Emerging Technologies
language:en
Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Zhu Feng,Ukkusuri Satish V.

Publisher

Elsevier BV

Subject

Computer Science Applications,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference42 articles.

1. Integration of environmental objectives in a system optimal dynamic traffic assignment model;Aziz;Comput.-Aided Civil Infrastruct. Eng.,2012

2. Aziz, H., Zhu, F., Ukkusuri, S.V., 2013. Reinforcement learning based signal control using R-Markov Average Reward Technique (RMART) accounting for neighborhood congestion information sharing. In: Proceedings of 92nd Transportation Research Board Meeting, National Academies (Washington, D.C., January 2013).

3. Brilon, W., Geistefeldt, J., Regler, M., 2005. Reliability of freeway traffic flow: a stochastic concept of capacity. In: Proceedings of the 16th International Symposium on Transportation and Traffic Theory, College Park, Maryland, pp. 125–144.

4. Implementing the concept of reliability for highway capacity analysis;Brilon;Trans. Res. Rec.: J. Transport. Res. Board,2007

5. Optimal motorway traffic flow control involving variable speed limits and ramp metering;Carlson;Transport. Sci.,2010

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