DEUSTO research: 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, named 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 minutes. 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.

 

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