Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6936740 | Transportation Research Part C: Emerging Technologies | 2015 | 10 Pages |
Abstract
Transportation modelers are frequently faced with several optimization challenges related to model selection and parameter optimization for forecasting. The concept of surrogate modeling is discussed in order to tackle some limitations related to the practice of developing short-term forecasting algorithms. An automated meta-modeling technique is presented that uses heterogeneous information from multiple types of statistical and computationally intelligent models, along with multi-objective evolutionary strategies to optimize the model and parameter selection. A number of different models from the family of Support Vector Machines, Radial Base Functions and Neural Networks are jointly considered and optimized with the aim to improve the short-term predictability of travel speed. Results are presented and discussed in both a univariate and multivariate framework.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Eleni I. Vlahogianni,