Abstract
Road automobiles are deemed one of the major resources of energy consumption throughout cities. To realize and design sustainable urban transport, it is essential to comprehend as well as evaluate interactions among a set of elements, which form transport impacts and behaviors. The goal of the current research was to propose a hybrid algorithm, Artificial Neural Network (ANN)-Genetic Algorithm (ANN-GA), ANN-Simulated Annealing (ANN-SA), and Particle Swarm Optimization (ANN-PSO) to better optimize the coefficients for predicting the energy demand based on the several predictor variables (1975–2019) i.e., GDP, year, vehicle-km, population, oil price, passenger-km, and ton-km in Turkey. Eleven combinations of all predictor variables were selected and then compared with real data. The outcomes exposed that the proposed ANN-PSO technique based on the GDP, population, ton-km outperforms the other two models. It is anticipated that this research can be useful for developing extremely productive and applicable planning regarding transportation energy policies.
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