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Authors: Junping Xiang 1 and Zonghai Chen 2

Affiliations: 1 University of Science and Technology of China, Lianyungang JARI Electronics Co. and Ltd. of CSIC, China ; 2 University of Science and Technology of China, China

ISBN: 978-989-758-077-2

ISSN: 2184-4313

Keyword(s): Grey Qualitative, Reinforcement Learning, Bottleneck Subzone Control, BP Neural Networks.

Related Ontology Subjects/Areas/Topics: Applications ; Learning and Adaptive Control ; Learning in Process Automation ; Pattern Recognition ; Software Engineering

Abstract: A Grey Qualitative Reinforment Learning algorithm is present in this paper to realize the adaptive signal control of bottleneck subzone, which is described as a nonlinear optimization problem. In order to handle the uncertainites in the traffic flow system, grey theory model and qualitative method were used to express the sensor data. In order to avoid deducing the function relationship of the traffic flow and the timing plan, grey reinforcement learning algorithm, which is the biggest innovation in this paper, was proposed to seek the solution. In order to enhance the generalization capability of the system and avoid the "curse of dimensionality" and improve the convergence speed, BP neural network was used to approximate the Q-function. We do three simulation experiments (calibrated with real data) using four evaluation indicators for contrast and analyze. Simulation results show that the proposed method can significantly improve the traffic situation of bottleneck subzone, and the algorithm has good robustness and low noise sensitivity. (More)

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Paper citation in several formats:
Xiang, J. and Chen, Z. (2015). Adaptive Traffic Signal Control of Bottleneck Subzone based on Grey Qualitative Reinforcement Learning Algorithm.In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, ISSN 2184-4313, pages 295-301. DOI: 10.5220/0005269302950301

@conference{icpram15,
author={Junping Xiang. and Zonghai Chen.},
title={Adaptive Traffic Signal Control of Bottleneck Subzone based on Grey Qualitative Reinforcement Learning Algorithm},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={295-301},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005269302950301},
isbn={978-989-758-077-2},
}

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - Adaptive Traffic Signal Control of Bottleneck Subzone based on Grey Qualitative Reinforcement Learning Algorithm
SN - 978-989-758-077-2
AU - Xiang, J.
AU - Chen, Z.
PY - 2015
SP - 295
EP - 301
DO - 10.5220/0005269302950301

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