A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy
| Autor: | Antonio D. Masegosa, Eneko Osaba, Pedro Lopez-Garcia, Enrique Onieva, Asier Perallos |
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| Rok vydání: | 2016 |
| Předmět: |
050210 logistics & transportation
Artificial neural network Computer science Mechanical Engineering 05 social sciences Traffic simulation 02 engineering and technology Fuzzy control system computer.software_genre Fuzzy logic Computer Science Applications Cross entropy Traffic congestion 11. Sustainability 0502 economics and business Automotive Engineering Genetic algorithm 0202 electrical engineering electronic engineering information engineering Entropy (information theory) 020201 artificial intelligence & image processing Data mining computer Algorithm |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems |
| ISSN: | 1558-0016 1524-9050 |
| DOI: | 10.1109/tits.2015.2491365 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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