scholarly journals An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Zhanzhong Wang ◽  
Ruijuan Chu ◽  
Minghang Zhang ◽  
Xiaochao Wang ◽  
Siliang Luan
Keyword(s):  
Traffic Flow ◽  
Ensemble Learning ◽  
Learning Method ◽  
Flow State ◽  
Highway Traffic ◽  
State Identification ◽  
Selective Ensemble

scholarly journals Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hua-pu Lu ◽  
Zhi-yuan Sun ◽  
Wen-cong Qu
Keyword(s):  
Big Data ◽  
Real Time ◽  
Traffic Flow ◽  
Optimization Model ◽  
Bilevel Optimization ◽  
Data Driven ◽  
Stage Model ◽  
Flow State ◽  
Real Time Traffic ◽  
State Identification

With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzyc-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

scholarly journals Selective ensemble learning method for belief-rule-base classification system based on PAES

2019 ◽  
Vol 2 (4) ◽  
pp. 306-318 ◽  
Author(s):  
Wanling Liu ◽  
Weikun Wu ◽  
Yingming Wang ◽  
Yanggeng Fu ◽  
Yanqing Lin
Keyword(s):  
Ensemble Learning ◽  
Classification System ◽  
Rule Base ◽  
Learning Method ◽  
Belief Rule Base ◽  
Selective Ensemble

The Analysis of Traffic Flow State on Foggy Expressway Based on Fuzzy Clustering

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Jianjun Wang ◽  
Chenfeng Xie ◽  
Zhenwen Chang ◽  
Jingjing Zhang
Keyword(s):  
Traffic Flow ◽  
Fuzzy Clustering ◽  
Flow State

A Survey of Selective Ensemble Learning Algorithms

2011 ◽  
Vol 34 (8) ◽  
pp. 1399-1410 ◽  
Author(s):  
Chun-Xia ZHANG ◽  
Jiang-She ZHANG
Keyword(s):  
Ensemble Learning ◽  
Learning Algorithms ◽  
Selective Ensemble
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