While information technology has facilitated the collection of neverbefore-seen quantities of data, these data have not always provided the information needed by transportation professionals to support sound decision making. Computational intelligence (CI) has great potential to support the needs of transportation professionals. CI is a result of synergy among information processing technologies such as artificial neural networks (ANNs), fuzzy sets, and genetic algorithms. As the number of CI applications to transportation problems grows, so does the need to evaluate these systems. The issue of validating and evaluating transportation CI applications is addressed. A case study that evaluates the effectiveness of two CI paradigms, case-based reasoning and ANNs, for estimating the benefits of real-time traffic diversion is presented. The case study illustrates the need for regarding validation and evaluation as a part of the development effort and the need for tuning the design parameters of CI paradigms.
Toward More Effective Transportation Applications of Computational Intelligence Paradigms
Transportation Research Record: Journal of the Transportation Research Board
Transportation Research Record: Journal of the Transportation Research Board ; 1836 , 1 ; 57-63
2003-01-01
Article (Journal)
Electronic Resource
English
Toward More Effective Transportation Applications of Computational Intelligence Paradigms
Online Contents | 2003
|TIBKAT | 1975
Computational intelligence determines effective rationality
British Library Online Contents | 2008
|When paradigms converge:the exciting future of transportation electronics
Automotive engineering | 1991
|