Forecasting transportation infrastructure impacts of renewable energy industry using neural networks

Abstract

Iowa is a state rich in renewable energy resources, especially biomass. The successful development of renewable energy industry in Iowa is concomitant with increase in freight traffic and is likely to have significant impacts on transportation infrastructure condition and increased maintenance expenses for the state and local governments. The primary goal of this paper is to investigate the feasibility of employing the Neural Networks (NN) methodology to forecast the impacts of Iowa's biofuels and wind power industries on Iowa's secondary and local road condition and maintenance-related costs in a panel data framework. The data for this study were obtained from a number of sources and for a total of 24 counties in clusters in Northern, Western, and Southern Iowa over a period of ten years. Back-Propagation NN (BPNN) using a Quasi-Newton secondorder training algorithm was chosen for this study owing to its very fast convergence properties. Since the size of the training set is relatively small, ensembles of well-trained NNs were formed to achieve significant improvements in generalization performance. The developed NN forecasting models could identify the presence of biofuel plants and wind farms as well as large-truck traffic as the most sensitive inputs influencing pavement condition and granular and blading maintenance costs. Pavement deterioration resulting from traffic loads was found to be associated with the presence of both biofuel plants and wind farms. The developed NN forecasting models can be useful in identifying and properly evaluating future transportation infrastructure impacts resulting from the renewable energy industry development and thus help Iowa maintain its competitive edge in the rapidly developing bioeconomy.

Keywords:

• neural networks
• renewable energy
• biofuel plants
• wind farms
• forecasting
• panel data
• infrastructure impacts

JEL Classification:

• C45
• C80
• O18
• R42

Additional information

Notes on contributors

Kasthurirangan Gopalakrishnan

Kasthurirangan GOPALAKRISHNAN. Research Assistant Professor in the Department of Civil Construction, and Environmental Engineering at Iowa State University (ISU), Member of the American Society of Civil Engineers (ASCE) as well as an Editorial Board Member of the Transport and International Journal for Traffic and Transport Engineering. He received the Dwight D. Eisenhower Transportation Fellowship award in 1999 and he completed his PhD in 2004 at the University of Illinois at Urbana-Champaign, USA. His research interests include the use of bio-inspired computing paradigms in civil engineering informatics, sustainable and green technologies, simulation, imaging, and mechanics of transportation infrastructure systems.

Konstantina “Nadia” Gkritza

Konstantina “Nadia” GKRITZA. Assistant Professor in the Department of Civil Construction, and Environmental Engineering at Iowa State University (ISU), Associate Director of the Mid-American Transportation Center as well as Associate Editor of the American Society of Civil Engineers (ASCE) Journal of Transportation Engineering. She completed her PhD in 2006 at Purdue University, West Lafayette, USA. She has received several awards including Schafer Award for Excellence in Teaching, Research, and Service at ISU, C.V. Wootan Memorial Award for outstanding PhD dissertation in Policy and Planning. Her research interests include transportation planning and systems evaluation, transportation economics, transportation energy and sustainability, highway safety, and (critical) infrastructure management.