A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy

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.

Document type: Article

Full document

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1109/tits.2015.2491365 under the license https://creativecommons.org/licenses/by-nc-nd

• https://zenodo.org/record/164838

• https://zenodo.org/record/164838/files/Lopez_GACE.pdf under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

• http://xplorestaging.ieee.org/ielx7/6979/7394960/07331637.pdf?arnumber=7331637,

http://dx.doi.org/10.1109/tits.2015.2491365 under the license cc-by-nc-nd

• https://dblp.uni-trier.de/db/journals/tits/tits17.html#Lopez-GarciaOOM16,

https://ieeexplore.ieee.org/document/7331637,

http://ieeexplore.ieee.org/document/7331637,

https://doi.org/10.1109/TITS.2015.2491365,

https://trid.trb.org/view.aspx?id=1397289,

https://academic.microsoft.com/#/detail/2274750026