[1] Bie Yiming, Wang Dianhai, Ma Dongfang, Zhu Zibo, et al. A distributed algorithm for signal coordination of multiple agentswith embedded platoon dispersion model [J]. Journal of Southeast University (English Edition), 2011, 27 (3): 311-315. [doi:10.3969/j.issn.1003-7985.2011.03.017]

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A distributed algorithm for signal coordination of multiple agentswith embedded platoon dispersion model()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

27

Issue:

2011 3

Page:

311-315

Research Field:

Traffic and Transportation Engineering

Publishing date:

2011-09-30

Info

Title:

A distributed algorithm for signal coordination of multiple agentswith embedded platoon dispersion model

Author(s):

Bie Yiming¹;  Wang Dianhai²;  Ma Dongfang¹;  Zhu Zibo¹

¹ College of Transportation, Jilin University, Changchun 130025, China
² College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

Keywords:

multiple agents;  signal coordination;  reinforce learning;  platoon dispersion model

PACS:

U491

DOI:

10.3969/j.issn.1003-7985.2011.03.017

Abstract:

In order to reduce average arterial vehicle delay, a novel distributed and coordinated traffic control algorithm is developed using the multiple agent system and the reinforce learning(RL). The RL is used to minimize average delay of arterial vehicles by training the interaction ability between agents and exterior environments. The Robertson platoon dispersion model is embedded in the RL algorithm to precisely predict platoon movements on arteries and then the reward function is developed based on the dispersion model and delay equations cited by HCM2000. The performance of the algorithm is evaluated in a Matlab environment and comparisons between the algorithm and the conventional coordination algorithm are conducted in three different traffic load scenarios. Results show that the proposed algorithm outperforms the conventional algorithm in all the scenarios. Moreover, with the increase in saturation degree, the performance is improved more significantly. The results verify the feasibility and efficiency of the established algorithm.

References:

[1] National Transportation Operations Coalition. National traffic signal report card [R]. Washington, DC: National Research Council, 2005.
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[9] Ma Shoufeng, Li Ying, Liu Bao. Agent-based learning control method for urban traffic signal of single intersection [J]. Journal of System Engineering, 2003, 18(3):273-278.(in Chinese)
[10] Abdulhai B. Reinforcement learning for the true adaptive traffic signal control [J]. Journal of Transportation Engineering, 2003, 129(3):278-285.
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[12] Wiering M. Multi-agent reinforcement learning for traffic light control [C]// Proceedings of the 17th International Conference on Machine Learning. San Francisco, CA, USA, 2000: 1151-1158.
[13] Wang Dianhai. Traffic flow theory [M]. Beijing: China Communications Press, 2002:146-147.(in Chinese)
[14] Transportation Research Board. Highway capacity manual [R]. Washington, DC: National Research Council, 2000.

Memo

Memo:

Biographies: Bie Yiming(1986—), male, graduate; Wang Dianhai(corresponding author), male, professor, wangdianhai@sohu.com.
Foundation items: The National Key Technology R&D Program during the 11th Five-Year Plan Period of China(No.2009BAG17B02), the National High Technology Research and Development Program of China(863 Program)(No.2011AA110304), the National Natural Science Foundation of China(No.50908100).
Citation: Bie Yiming, Wang Dianhai, Ma Dongfang, et al. A distributed algorithm for signal coordination of multiple agents with embedded platoon dispersion model[J].Journal of Southeast University(English Edition), 2011, 27(3):311-315.[doi:10.3969/j.issn.1003-7985.2011.03.017]

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