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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.