Abstract

WOS: 000369198300006 Predicting the fuel consumption of transport aircraft is vital for minimizing the detrimental effects of fuel emissions on the environment, saving fuel energy sources, reducing flight costs, achieving more accurate aircraft trajectory prediction, and providing effective and seamless management of air traffic. In this study, a genetic algorithm-optimized neural network topology is designed to predict the fuel flow-rate of a transport aircraft using real flight data. This model incorporates the cruise flight phase and the fuel consumption dependency with respect to both the variation of true airspeed and altitude. Feed-forward backpropagation and Levenberg-Marquardt algorithms are applied, and a genetic algorithm is utilized to design the optimum network architecture regarding time and effort. The predicted fuel flow-rates closely match the real data for both neural network training algorithms. Backpropagation gives the best accuracy for the climb and cruise phases, whereas the Levenberg-Marquardt algorithm is optimal for the descent phase

Abstract

This study proposes Reinforcement Learning (RL) based algorithm for finding optimum signal timings in Coordinated Signalized Networks (CSN) for fixed set of link flows. For this purpose, MOdified REinforcement Learning algorithm with TRANSYT-7F (MORELTRANS) model is proposed by way of combining RL algorithm and TRANSYT-7F. The modified RL differs from other RL algorithms since it takes advantage of the best solution obtained from the previous learning episode by generating a sub-environment at each learning episode as the same size of original environment. On the other hand, TRANSYT-7F traffic model is used in order to determine network performance index, namely disutility index. Numerical application is conducted on medium sized coordinated signalized road network. Results indicated that the MORELTRANS produced slightly better results than the GA in signal timing optimization in terms of objective function value while it outperformed than the HC. In order to show the capability of the proposed model for heavy demand condition, two cases in which link flows are increased by 20% and 50% with respect to the base case are considered. It is found that the MORELTRANS is able to reach good solutions for signal timing optimization even if demand became increased. (C) 2015 Elsevier Ltd. All rights reserved.