Back to Search
Start Over
A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy.
- Source :
- IEEE Transactions on Intelligent Transportation Systems; Feb2016, Vol. 17 Issue 2, p557-569, 13p
- Publication Year :
- 2016
-
Abstract
- This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min. A comparative study of different levels of hybridization in GACE is made. These range from a pure GA to a pure CE, passing through different weights for each of the combined techniques. The results prove that GACE is more accurate than GA or CE alone for predicting short-term traffic congestion. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 15249050
- Volume :
- 17
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Intelligent Transportation Systems
- Publication Type :
- Academic Journal
- Accession number :
- 112816096
- Full Text :
- https://doi.org/10.1109/TITS.2015.2491365