scholarly journals A Two-Phase Approach for Predicting Highway Passenger Volume

2021 ◽  
Vol 11 (14) ◽  
pp. 6248
Author(s):  
Yun Xiang ◽  
Jingxu Chen ◽  
Weijie Yu ◽  
Rui Wu ◽  
Bing Liu ◽  
...  
Keyword(s):  
Regional Integration ◽  
Continuous Process ◽  
Transportation Systems ◽  
Impact Factors ◽  
Support Vector ◽  
Two Phase ◽  
Phase Approach ◽  
Regression Methods ◽  
Passenger Volume ◽  
Highly Correlated

With the continuous process of urbanization, regional integration has become an inevitable trend of future social development. Accurate prediction of passenger volume is an essential prerequisite for understanding the extent of regional integration, which is one of the most fundamental elements for the enhancement of intercity transportation systems. This study proposes a two-phase approach in an effort to predict highway passenger volume. The datasets subsume highway passenger volume and impact factors of urban attributes. In Phase I, correlation analysis is conducted to remove highly correlated impact factors, and a random forest algorithm is employed to extract significant impact factors based on the degree of impact on highway passenger volume. In Phase II, a deep feedforward neural network is developed to predict highway passenger volume, which proved to be more accurate than both the support vector machine and multiple regression methods. The findings can provide useful information for guiding highway planning and optimizing the allocation of transportation resources.

scholarly journals Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
I-Chen Chen ◽  
Philip M. Westgate
Keyword(s):  
Parameter Estimation ◽  
Longitudinal Data ◽  
Quantile Regression ◽  
Mean Squared Error ◽  
Time Dependent ◽  
Regression Methods ◽  
Squared Error ◽  
Highly Correlated ◽  
Time Dependent Covariates ◽  
Independence Structure

AbstractWhen observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.

A two-phase approach for mining weighted partial periodic patterns

2014 ◽  
Vol 30 ◽  
pp. 225-234 ◽  
Author(s):  
Kung-Jiuan Yang ◽  
Tzung-Pei Hong ◽  
Guo-Cheng Lan ◽  
Yuh-Min Chen
Keyword(s):  
Periodic Patterns ◽  
Two Phase ◽  
Phase Approach

scholarly journals Multiphase gas-flow model of an electrical submersible pump

2018 ◽  
Vol 73 ◽  
pp. 29
Author(s):  
Diana Marcela Martinez Ricardo ◽  
German Efrain Castañeda Jiménez ◽  
Janito Vaqueiro Ferreira ◽  
Pablo Siqueira Meirelles
Keyword(s):  
Gas Flow ◽  
Oil And Gas ◽  
Learning Algorithm ◽  
Estimation Error ◽  
Oil And Gas Industry ◽  
Support Vector ◽  
System Response ◽  
Submersible Pump ◽  
Two Phase ◽  
Electrical Submersible Pump

Various artificial lifting systems are used in the oil and gas industry. An example is the Electrical Submersible Pump (ESP). When the gas flow is high, ESPs usually fail prematurely because of a lack of information about the two-phase flow during pumping operations. Here, we develop models to estimate the gas flow in a two-phase mixture being pumped through an ESP. Using these models and experimental system response data, the pump operating point can be controlled. The models are based on nonparametric identification using a support vector machine learning algorithm. The learning machine’s hidden parameters are determined with a genetic algorithm. The results obtained with each model are validated and compared in terms of estimation error. The models are able to successfully identify the gas flow in the liquid-gas mixture transported by an ESP.

scholarly journals Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor

2014 ◽  
Vol 14 (4) ◽  
pp. 219-226 ◽  
Author(s):  
Dongzhi Zhang ◽  
Bokai Xia
Keyword(s):  
Water Content ◽  
Measurement Model ◽  
Support Vector ◽  
Svm Classifier ◽  
Model Parameters ◽  
Water Mixture ◽  
Measuring Range ◽  
Two Phase ◽  
Soft Measurement ◽  
Oil Water

Abstract Measurement of water content in oil-water mixing flow was restricted by special problems such as narrow measuring range and low accuracy. A simulated multi-sensor measurement system in the laboratory was established, and the influence of multi-factor such as temperature, and salinity content on the measurement was investigated by numerical simulation combined with experimental test. A soft measurement model based on rough set-support vector machine (RS-SVM) classifier and genetic algorithm-neural network (GA-NN) predictors was reported in this paper. Investigation results indicate that RS-SVM classifier effectively realized the pattern identification for water holdup states via fuzzy reasoning and self-learning, and GA-NN predictors are capable of subsection forecasting water content in the different water holdup patterns, as well as adjusting the model parameters adaptively in terms of online measuring range. Compared with the actual laboratory analyzed results, the soft model proposed can be effectively used for estimating the water content in oil-water mixture in all-round measuring range

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