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

被引：101

作者：

Lopez-Garcia, Pedro ^{[1
]}

Onieva, Enrique ^{[1
]}

Osaba, Eneko ^{[1
]}

Masegosa, Antonio D. ^{[1
,2
]}

Perallos, Asier ^{[1
]}

机构：

[1] Univ Deusto, Deusto Inst Technol, Bilbao 48007, Spain

[2] Basque Fdn Sci, Ikerbasque, Bilbao 48013, Spain

来源：

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | 2016年 / 17卷 / 02期

基金：

欧盟地平线“2020”;

关键词：

Intelligent transportation systems; genetic algorithms; cross entropy; hierarchical fuzzy-rule-based systems; traffic congestion prediction; curse of dimensionality; fuzzy logic; fuzzy systems; FUZZY-SYSTEMS; NEURAL-NETWORK; OPTIMIZATION; MODEL; CLASSIFICATION; DEMAND;

D O I：

10.1109/TITS.2015.2491365

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

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.

引用

页码：557 / 569

页数：13

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