Chapter
Jan 24, 2013
Forecasting of National Expressway Network Scale Based on Support Vector Regression with Adaptive Genetic Algorithm
Authors: Xia Luo [email protected], Hongfei Ding [email protected]., and Lingwan Zhang [email protected]Author Affiliations
Publication: ICLEM 2012: Logistics for Sustained Economic Development—Technology and Management for Efficiency
Abstract
Forecasting of national expressway network scale is an important part of expressway network planning. It is a complicated problem due to its strong nonlinearity and small quantity of training data. Firstly, An AGA-SVR forecasting model is proposed by combining the support vector regression (SVR) and adaptive genetic algorithm (AGA) in this paper. Support vector regression (SVR) has been successfully employed to solve regression problems of nonlinearity and problem samples, but selecting appropriate parameters, which is a combinatorial optimization problem, is very crucial to learning performance and generalization performance of SVR. An adaptive genetic algorithm is used to determine the free parameters of support vector regression. Finally, examples of the scale data of China national expressway network are used to illustrate the performance of the proposed AGA-SVR model. Experimental results demonstrate that the AGA-SVR outperforms the BP neural-network; the AGA-SVR model is a reasonable alternative to forecast national expressway network scale.
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© 2012 American Society of Civil Engineers.
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Published online: Jan 24, 2013
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School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China, 610031.E-mail: [email protected]
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China, 610031.E-mail: [email protected].
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China, 610031.E-mail: [email protected]
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