Use of artificial neural networks for transport energy demand modeling |
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| Affiliation: | 1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;2. Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA;1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China;2. School of Computer Science and Informatics, De Montfort University, Leicester, UK;1. Ingenium Research Group, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain;2. CUNEF-Ingenium, CUNEF, Madrid, Spain;1. Department of Economic, Alzahra University, Iran;2. Department of Economic, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran |
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| Abstract: | The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem. |
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