Forecasting Travel Demand When the Explanatory Variables Are Highly Correlated

1980 ◽  
Vol 18 (4) ◽  
pp. 31-34 ◽  
Author(s):  
Edwin T. Fujii ◽  
James Mak
Keyword(s):  
Travel Demand ◽  
Explanatory Variables ◽  
Highly Correlated

scholarly journals A new modified ridge-type estimator for the beta regression model: simulation and application

2021 ◽  
Vol 7 (1) ◽  
pp. 1035-1057
Author(s):  
Muhammad Nauman Akram ◽  
◽  
Muhammad Amin ◽  
Ahmed Elhassanein ◽  
Muhammad Aman Ullah ◽  
...  
Keyword(s):  
Regression Model ◽  
Mean Squared Error ◽  
Model Simulation ◽  
Beta Regression ◽  
Explanatory Variables ◽  
Squared Error ◽  
The Matrix ◽  
Biased Estimators ◽  
Highly Correlated ◽  
Beta Regression Model

<abstract> <p>The beta regression model has become a popular tool for assessing the relationships among chemical characteristics. In the BRM, when the explanatory variables are highly correlated, then the maximum likelihood estimator (MLE) does not provide reliable results. So, in this study, we propose a new modified beta ridge-type (MBRT) estimator for the BRM to reduce the effect of multicollinearity and improve the estimation. Initially, we show analytically that the new estimator outperforms the MLE as well as the other two well-known biased estimators i.e., beta ridge regression estimator (BRRE) and beta Liu estimator (BLE) using the matrix mean squared error (MMSE) and mean squared error (MSE) criteria. The performance of the MBRT estimator is assessed using a simulation study and an empirical application. Findings demonstrate that our proposed MBRT estimator outperforms the MLE, BRRE and BLE in fitting the BRM with correlated explanatory variables.</p> </abstract>

Scale Matters: Addressing the Limited Robustness of Findings on Negative Advertising

2013 ◽  
Vol 14 (4) ◽  
pp. 521-541
Author(s):  
ELIYAHU V. SAPIR ◽  
JONATHAN SULLIVAN ◽  
TIM VEEN
Keyword(s):  
Negative Advertising ◽  
Negative Campaigning ◽  
Explanatory Variables ◽  
Campaign Ads ◽  
Alternative Measures ◽  
Highly Correlated ◽  
Negative Messages ◽  
Campaign Advertising

AbstractNegative campaign advertising is a major component of the electoral landscape, and has received much attention in the literature. In many studies, political scientists have tried to explain why some campaign ads contain more negative messages than others and to identify the determinants of this form of campaign behavior. In recent years, a number of studies have acknowledged the differences between alternative measures of negativity, but, in most cases, it is assumed that since these measures are highly correlated, they are unidimensional and essentially interchangeable. In this article, we argue that much of the debate in the literature over negative campaigning is a result of inadequate operationalizations of negativity. Although debates over negativity have often been framed in conceptual terms, there is a methodological explanation for why they persist We begin our analysis by constructing reliable scales of negativity, and model them with salient predictors reported in the literature as significantly associated with campaign attacks. Our findings show that scaling does matter, and while some of the explanatory variables are robust predictors of negativity, most of them are not.

scholarly journals National culture dimensions as predictors of innovation, creativity, and entrepreneurship

2021 ◽  
Vol 6 (2) ◽  
pp. 154-171
Author(s):  
Louis Jourdan ◽  
Michael Smith
Keyword(s):  
Economic Growth ◽  
National Culture ◽  
Cultural Dimensions ◽  
Explanatory Variables ◽  
Global Innovation ◽  
Culture Dimensions ◽  
Research Gap ◽  
Highly Correlated ◽  
Major Factors ◽  
Criterion Development

The purposes of this study were twofold. The first was to encourage other investigators to examine more closely three indices related to economic growth, specifically innovation, entrepreneurship, and creativity. The second was to encourage further investigation of Hofstede’s national culture as explanatory variables. This investigation addressed this research gap by examining the relationships among indices of nations’ creativity, entrepreneurship, and innovation, and their relationships with Hofstede’s (2015) national culture dimensions. No previous research was identified which examined countries’ creativity, entrepreneurship, and innovation in the same study. The relationships among four measures associated with economic development—the Global Innovation Index (GII), the Global Entrepreneurship Index (GEI), the Global Creativity Index (GCI), and Bloomberg 50 most innovative countries (B50) were studied. Two rarely investigated indices (B50 and GCI) were included in this research. Results indicated that all four indices were highly correlated. The factor structure of Hofstede’s six cultural dimensions was reduced to three major factors: heteronomy-autonomy, gratification, and competition-altruism. Using multiple regression analysis, heteronomy-autonomy and gratification predicted GII. Gratification predicted the remaining three criteria. This study addressed this research gap of criterion development by examining the relationships among these variables, their relationships with national culture, and their predictability from different national culture dimensions. Practical implications of these findings for decision-makers and policymakers who want to increase their country’s economic growth through the support of creativity, innovation, and entrepreneurship were discussed.

scholarly journals Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256610
Author(s):  
Xingpei Yan ◽  
Zheng Zhu
Keyword(s):  
Traffic Safety ◽  
Traffic Congestion ◽  
Regression Models ◽  
Travel Demand ◽  
The Other ◽  
Explanatory Variables ◽  
Safety Issues ◽  
Country Level ◽  
The Impact ◽  
Cluster 2

The impacts of COVID-19 on travel demand, traffic congestion, and traffic safety are attracting heated attention. However, the influence of the pandemic on electric bike (e-bike) safety has not been investigated. This paper fills the research gap by analyzing how COVID-19 affects China’s e-bike safety based on a province-level dataset containing e-bike safety metrics, socioeconomic information, and COVID-19 cases from 2017 to 2020. Multi-output regression models are adopted to investigate the overall impact of COVID-19 on e-bike safety in China. Clustering-based regression models are used to examine the heterogeneous effects of COVID-19 and the other explanatory variables in different provinces/municipalities. This paper confirms the high relevance between COVID-19 and the e-bike safety condition in China. The number of COVID-19 cases has a significant negative effect on the number of e-bike fatalities/injuries at the country level. Moreover, two clusters of provinces/municipalities are identified: one (cluster 1) with lower and the other (cluster 2 that includes Hubei province) higher number of e-bike fatalities/injuries. In the clustering-based regressions, the absolute coefficients of the COVID-19 feature for cluster 2 are much larger than those for cluster 1, indicating that the pandemic could significantly reduce e-bike safety issues in provinces with more e-bike fatalities/injuries.

Classification of Wines Using Principal Component Analysis

2021 ◽  
Vol 16 (1) ◽  
pp. 56-67 ◽  
Author(s):  
Jackson Barth ◽  
Duwani Katumullage ◽  
Chenyu Yang ◽  
Jing Cao
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Nearest Neighbor ◽  
Predictive Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Explanatory Variables ◽  
Highly Correlated

AbstractClassification of wines with a large number of correlated covariates may lead to classification results that are difficult to interpret. In this study, we use a publicly available dataset on wines from three known cultivars, where there are 13 highly correlated variables measuring chemical compounds of wines. The goal is to produce an efficient classifier with straightforward interpretation to shed light on the important features of wines in the classification. To achieve the goal, we incorporate principal component analysis (PCA) in the k-nearest neighbor (kNN) classification to deal with the serious multicollinearity among the explanatory variables. PCA can identify the underlying dominant features and provide a more succinct and straightforward summary over the correlated covariates. The study shows that kNN combined with PCA yields a much simpler and interpretable classifier that has comparable performance with kNN based on all the 13 variables. The appropriate number of principal components is chosen to strike a balance between predictive accuracy and simplicity of interpretation. Our final classifier is based on only two principal components, which can be interpreted as the strength of taste and level of alcohol and fermentation in wines, respectively. (JEL Classifications: C10, Cl4, D83)

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

2020 ◽  
Vol 2674 (8) ◽  
pp. 145-159 ◽  
Author(s):  
Ehsan Esmaeilzadeh ◽  
Seyedmirsajad Mokhtarimousavi
Keyword(s):  
Machine Learning ◽  
New York ◽  
Impact Analysis ◽  
Travel Demand ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Explanatory Variables ◽  
Explanatory Factors ◽  
Flight Delay ◽  
Delay Prediction

The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the U.S. economy. On‐time operations play a key role in airline performance and passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of major importance. The application of machine learning techniques in data mining has seen explosive growth in recent years and has garnered interest from a broadening variety of research domains including aviation. This study employed a support vector machine (SVM) model to explore the non-linear relationship between flight delay outcomes. Individual flight data were gathered from 20 days in 2018 to investigate causes and patterns of air traffic delay at three major New York City airports. Considering the black box characteristic of the SVM, a sensitivity analysis was performed to assess the relationship between dependent and explanatory variables. The impacts of various explanatory variables are examined in relation to delay, weather information, airport ground operation, demand-capacity, and flow management characteristics. The variable impact analysis reveals that factors such as pushback delay, taxi-out delay, ground delay program, and demand-capacity imbalance with the probabilities of 0.506, 0.478, 0.339, and 0.338, respectively, are significantly associated with flight departure delay. These findings provide insight for better understanding of the causes of departure delays and the impacts of various explanatory factors on flight delay patterns.

scholarly journals New robust-ridge estimators for partially linear model

2019 ◽  
Vol 8 (2) ◽  
pp. 46
Author(s):  
Mervat M. Elgohary ◽  
Mohamed R. Abonazel ◽  
Nahed M. Helmy ◽  
Abeer R. Azazy
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Linear Model ◽  
Partially Linear Model ◽  
Least Trimmed Squares ◽  
Monte Carlo Simulation Study ◽  
Explanatory Variables ◽  
Partially Linear ◽  
Biased Estimators ◽  
Highly Correlated

This paper considers the partially linear model when the explanatory variables are highly correlated as well as the dataset contains outliers. We propose new robust biased estimators for this model under these conditions. The proposed estimators combine least trimmed squares and ridge estimations, based on the spline partial residuals technique. The performance of the proposed estimators and the Speckman-spline estimator has been examined by a Monte Carlo simulation study. The results indicated that the proposed estimators are more efficient and reliable than the Speckman-spline estimator.  

scholarly journals Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

2019 ◽  
Vol 11 (10) ◽  
pp. 2730 ◽  
Author(s):  
Ruone Zhang ◽  
Xin Ye ◽  
Ke Wang ◽  
Dongjin Li ◽  
Jiayu Zhu
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Low Cost ◽  
Mode Choice ◽  
Sampling Bias ◽  
Joint Model ◽  
Transit System ◽  
Explanatory Variables ◽  
Mode Choice Model ◽  
Acd Model

Travel data collection, which is necessary for travel demand modeling, is always of great concern to modelers due to its huge cost and effort when a large sample is required to achieve satisfactory model precisions. In this paper, travel data collected based on a survey questionnaire and travelers’ active participation are called actively collected data (ACD). It is difficult to guarantee absolute randomness and unbiasedness in a sample when the ACD are collected due to self-selection issues. The aim of this study is to improve the model precision at low cost by using passively collected data (PCD), such as in-vehicle GPS data and transit smart card data, to release sample size restriction and reduce sampling bias of ACD in a commute mode choice model. In an empirical study, a multinomial-logit-based joint model is developed for commute mode choice by integrating ACD and PCD based on the choice-based sampling theory. A comprehensive set of explanatory variables are specified through data integration. Both simulation and empirical results show great improvement in coefficient precisions in the proposed joint model, relative to those in the ACD model and PCD model. In this study, ACD and PCD samples of Shanghai are integrated in the joint model so that several significantly influential level-of-service attributes are identified for auto, rail, and bus modes, and their impacts on commute mode choice probabilities are quantified. The findings can aid in better evaluating the program to improve the existing transit system.

Disaggregate Attraction-End Choice Modeling: Formulation and Empirical Analysis

1998 ◽  
Vol 1645 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Chandra Bhat ◽  
Ajay Govindarajan ◽  
Vamsi Pulugurta
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Choice Models ◽  
Distribution Models ◽  
Explanatory Variables ◽  
Aggregate Modeling ◽  
Demand Models ◽  
Modeling Paradigm ◽  
Travel Decisions ◽  
Trip Production

For travel demand models to provide good forecasts, they must be causal; that is, the models should represent the travel decisions made by individuals (and households) and should incorporate important demographic and policy-sensitive explanatory variables. This recognition has led to a shift from the aggregate modeling paradigm to the disaggregate modeling paradigm, evident in the widespread use of disaggregate trip production and mode choice models in practice. However, this shift toward disaggregate procedures has not yet influenced the fundamental specification of trip attraction and distribution models employed in practice. Developed and estimated were disaggregate attraction-end choice models that will facilitate the replacement of the aggregate trip attraction and distribution models currently in use. The proposed disaggregate attraction-end choice model is compared with the disaggregate equivalent of the gravity model.

scholarly journals A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data

2019 ◽  
Vol 11 (21) ◽  
pp. 5950
Author(s):  
Zhenbo Lu ◽  
Zhen Long ◽  
Jingxin Xia ◽  
Chengchuan An
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mode Selection ◽  
Travel Mode ◽  
Mode Identification ◽  
Explanatory Variables ◽  
Navigation Data ◽  
Gis Data ◽  
Transport Modes ◽  
Land Use Functions

Identifying and detecting the travel mode and pattern of individual travelers is an important problem in transportation planning and policy making. Mobile-phone Signaling Data (MSD) have numerous advantages, including wide coverage and low acquisition cost, data stability and reliability, and strong real-time performance. However, due to their noisy and temporally irregular nature, extracting mobility information such as transport modes from these data is particularly challenging. This paper establishes a travel mode identification model based on the MSD combined with residents’ travel survey data, Geographic Information System (GIS) data, and navigation data. Using the data obtained from Kunshan, China in 2017, enriched with variables on the travel mode identification, the model achieved a high accuracy of 90%. The accuracy is satisfactory for all of the transport modes other than buses. Furthermore, among the explanatory variables such as the built environment factors (e.g., the coverage rate of a bus stop) are in general more significant, in contrast with other attributes. This indicates that the land use functions are more influential on the travel mode selection as well as the level of travel demand.

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