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Coordinated multiple ramps metering based on neuro-fuzzy adaptive dynamic programming

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3 Author(s)
Xuerui Bai ; Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences. 95 Zhongguancun East Road, Haidian District, Beijing 100190, China ; Dongbin Zhao ; Jianqiang Yi

This paper aims to efficiently deal with the problems of multiple ramps metering. A new method which is called neuro-fuzzy adaptive dynamic programming with eligibility traces (NFADP(lambda)) is proposed. With the introduction of neuro-fuzzy and eligibility traces, the performance of ADP is greatly enhanced. First of all, the expert experience is introduced to ADP, therefore the convergence of ADP is greatly reinforced. Second, with the learning strategy revised, the training of action network is accelerated. In order to achieve multiple ramps metering control, special performance index function is established in NFADP(lambda). Extensive simulation on a hypothetical freeway are carried out with NFADP(lambda), compared to ALINEA as a stand-alone strategy. Simulation results indicate that NFADP(lambda) have good performances in both alleviating stochastic variations of the traffic demand and congestion situations.

Published in:

2009 International Joint Conference on Neural Networks

Date of Conference:

14-19 June 2009