Investigation of Contributing Factors to Traffic Crash Severity in Southeast Texas Using Multiple Correspondence Analysis

Driving is the essential means of travel in Southeast Texas, a highly urbanized and populous area that serves as an economic powerhouse of the whole state. However, driving in Southeast Texas is subject to many risks as this region features a typical humid subtropical climate with long hot summers and short mild winters. Local drivers would encounter intense precipitation, heavy fog, strong sunlight, standing water, slick road surface, and even frequent extreme weather such as tropical storms, hurricanes and flood during their year-around travels. Meanwhile, research has revealed that the fatality rate per 100 million vehicle miles driven in urban Texas became considerably higher than national average since 2010, and no conclusive study has elucidated the association between Southeast Texas crash severity and potential contributing factors. This study used multiple correspondence analysis (MCA) to examine a group of contributing factors on how their combinatorial influences determine crash severity by creating combination clouds on a factor map. Results revealed numerous significant combinatorial effects. For example, driving in rain and extreme weather on a wet road surface has a higher chance in causing crashes that incur severe or deadly injuries. Besides, other contributing factors involving risky behavioral factors, road designs, and vehicle factors were well discussed. The research outcomes could inspire local traffic administration to take more effective countermeasures to systematically mitigate road crash severity.

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Informed Geoheritage Conservation: Determinant Analysis Based on Bibliometric and Sustainability Indicators Using Ordination Techniques

Land ◽

10.3390/land10050539 ◽

2021 ◽

Vol 10 (5) ◽

pp. 539

Author(s):

Boglárka Németh ◽

Károly Németh ◽

Jon N. Procter

Keyword(s):

Correspondence Analysis ◽

Conservation Planning ◽

Earth Science ◽

Multiple Correspondence Analysis ◽

Contributing Factors ◽

Usual Model ◽

Development Indicators ◽

Individual Variables ◽

Business As Usual ◽

World Development

Ordination methods are used in ecological multivariate statistics in order to reduce the number of dimensions and arrange individual variables along environmental variables. Geoheritage designation is a new challenge for conservation planning. Quantification of geoheritage to date is used explicitly for site selection, however, it also carries significant potential to be one of the indicators of sustainable development that is delivered through geosystem services. In order to achieve such a dominant position, geoheritage needs to be included in the business as usual model of conservation planning. Questions about the quantification process that have typically been addressed in geoheritage studies can be answered more directly by their relationships to world development indicators. We aim to relate the major informative geoheritage practices to underlying trends of successful geoheritage implementation through statistical analysis of countries with the highest trackable geoheritage interest. Correspondence analysis (CA) was used to obtain information on how certain indicators bundle together. Multiple correspondence analysis (MCA) was used to detect sets of factors to determine positive geoheritage conservation outcomes. The analysis resulted in ordination diagrams that visualize correlations among determinant variables translated to links between socio-economic background and geoheritage conservation outcomes. Indicators derived from geoheritage-related academic activity and world development metrics show a shift from significant Earth science output toward disciplines of strong international agreement such as tourism, sustainability and biodiversity. Identifying contributing factors to conservation-related decisions helps experts to tailor their proposals for required evidence-based quantification reports and reinforce the scientific significance of geoheritage.

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Identification of Contributing Factors in Vehicle Pedestrian Crashes in Chennai using Multiple Correspondence Analysis

Transportation Research Procedia ◽

10.1016/j.trpro.2020.08.104 ◽

2020 ◽

Vol 48 ◽

pp. 3486-3495

Author(s):

Priyadarshini Natarajan ◽

Sathish Kumar Sivasankaran ◽

Venkatesh Balasubramanian

Keyword(s):

Correspondence Analysis ◽

Multiple Correspondence Analysis ◽

Contributing Factors ◽

Pedestrian Crashes

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Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques and Artificial Neural Network: A Modeling Comparison

Sustainability ◽

10.3390/su13105670 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5670

Author(s):

Gholamreza Shiran ◽

Reza Imaninasab ◽

Razieh Khayamim

Keyword(s):

Neural Network ◽

Data Mining ◽

Artificial Neural Network ◽

Logistic Regression ◽

Decision Tree ◽

Multinomial Logistic Regression ◽

Crash Severity ◽

Traffic Crash ◽

Road Crash ◽

Mlr Model

The classification of vehicular crashes based on their severity is crucial since not all of them have the same financial and injury values. In addition, avoiding crashes by identifying their influential factors is possible via accurate prediction modeling. In crash severity analysis, accurate and time-saving prediction models are necessary for classifying crashes based on their severity. Moreover, statistical models are incapable of identifying the potential severity of crashes regarding influencing factors incorporated in models. Unlike previous research efforts, which focused on the limited class of crash severity, including property damage only (PDO), fatality, and injury by applying data mining models, the present study sought to predict crash frequency according to five severity levels of PDO, fatality, severe injury, other visible injuries, and complaint of pain. The multinomial logistic regression (MLR) model and data mining approaches, including artificial neural network-multilayer perceptron (ANN-MLP) and two decision tree techniques, (i.e., Chi-square automatic interaction detector (CHAID) and C5.0) are utilized based on traffic crash records for State Highways in California, USA. The comparison of the findings of the relative importance of ten qualitative and ten quantitative independent variables incorporated in CHAID and C5.0 indicated that the cause of the crash (X1) and the number of vehicles (X5) were known as the most influential variables involved in the crash. However, the cause of the crash (X1) and weather (X2) were identified as the most contributing variables by the ANN-MLP model. In addition, the MLR model showed that the driver’s age (X11) accounts for a larger proportion of traffic crash severity. Therefore, the sensitivity analysis demonstrated that C5.0 had the best performance for predicting road crash severity. Not only did C5.0 take a shorter time (0.05 s) compared to CHAID, MLP, and MLR, it also represented the highest accuracy rate for the training set. The overall prediction accuracy based on the training data was approximately 88.09% compared to 77.21 and 70.21% for CHAID and MLP models. In general, the findings of this study revealed that C5.0 can be a promising tool for predicting road crash severity.

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The Model of Severity Prediction of Traffic Crash on the Curve

Mathematical Problems in Engineering ◽

10.1155/2014/832723 ◽

2014 ◽

Vol 2014 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

Jian-feng Xi ◽

Hai-zhu Liu ◽

Wei Cheng ◽

Zhong-hao Zhao ◽

Tong-qiang Ding

Keyword(s):

Logistic Regression ◽

Likelihood Estimation ◽

Crash Severity ◽

Traffic Crashes ◽

Traffic Crash ◽

Contributory Factors ◽

Severity Prediction ◽

Traffic Environment ◽

Road Crash ◽

Road Segments

With the study of traffic crashes on curved road segments as the focus of research, a logistic regression based curve road crash severity prediction model was established based on a sample crash database of 20000 entries collected from 4 regions of China and 15 evaluation indicators involving driver, driving environment, and traffic environment factors. Maximum Likelihood Estimation and step-back technique were deployed for data analysis, the conclusion of which is that the three main contributory factors on curve road crash severity are weather, roadside protection facility, and pavement structure. Hosmer and Lemeshow tests were used to verify the reliability of the model, and the model variables were discussed to a certain degree as well.

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Multiple-correspondence analysis optical microscopy for determination of starch granules

Journal of Chemometrics ◽

10.1002/cem.1180060307 ◽

1992 ◽

Vol 6 (3) ◽

pp. 163-175 ◽

Cited By ~ 5

Author(s):

M. F. Devaux ◽

E. M. Qannari ◽

D. J. Gallant

Keyword(s):

Optical Microscopy ◽

Correspondence Analysis ◽

Multiple Correspondence Analysis ◽

Starch Granules

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Multiple Correspondence Analysis

An Introduction to Correspondence Analysis - Wiley Series in Probability and Statistics ◽

10.1002/9781119044482.ch6 ◽

2021 ◽

pp. 143-161

Keyword(s):

Correspondence Analysis ◽

Multiple Correspondence Analysis

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Patterns of Italian high school and university students’ attitudes towards physics: an analysis based on semiotic-cultural perspective

European Journal of Psychology of Education ◽

10.1007/s10212-021-00563-z ◽

2021 ◽

Author(s):

Italo Testa ◽

Raffaele De Luca Picione ◽

Umberto Scotti di Uccio

Keyword(s):

High School ◽

Cluster Analysis ◽

University Students ◽

Correspondence Analysis ◽

Multiple Correspondence Analysis ◽

Psychological Theory ◽

Cultural Perspective ◽

Undergraduate Level ◽

Students Attitudes ◽

And Cluster Analysis

AbstractThe purpose of this study was to analyse Italian high school and university students’ attitudes towards physics using the Semiotic Cultural Psychological Theory (SCPT). In the SCPT framework, attitudes represent how individuals interpret their experience through the mediation of generalized meaning with which they are identified. A view-of-physics questionnaire was used as an instrument to collect data with 1603 high school and university students. Data were analysed through multiple correspondence analysis and cluster analysis. We identified four generalized meanings of physics: (a) interesting and important for society; (b) a quite interesting, but badly taught subject at school and not completely useful for society; (c) difficult to study and irrelevant for society; and (d) a fascinating and protective niche from society. The identified generalized meanings are significantly correlated to the choice to study physics at undergraduate level and to the choice of attending physics-related activities in high school. Implications for research are discussed.

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Effectiveness of mass treatment of Schistosoma mansoni infection in socially vulnerable areas of a state in northeastern Brazil, 2011–2014

Archives of Public Health ◽

10.1186/s13690-021-00549-9 ◽

2021 ◽

Vol 79 (1) ◽

Author(s):

Flávia Silvestre Outtes Wanderley ◽

Ulisses Montarroyos ◽

Cristine Bonfim ◽

Carolina Cunha-Correia

Keyword(s):

Schistosoma Mansoni ◽

Correspondence Analysis ◽

Social Vulnerability ◽

Multiple Correspondence Analysis ◽

Vulnerability Index ◽

Two Dimensions ◽

Northeastern Brazil ◽

Mass Treatment ◽

Social Vulnerability Index ◽

Treatment Information

Abstract Background To assess the effectiveness of mass treatment of Schistosoma mansoni infection in socially vulnerable endemic areas in northeastern Brazil. Method An ecological study was conducted, in which 118 localities in 30 municipalities in the state of Pernambuco were screened before 2011 and in 2014 (after mass treatment). Information on the endemic baseline index, mass treatment coverage, socio-environmental conditions and social vulnerability index were used in the multiple correspondence analysis. One hundred fourteen thousand nine hundred eighty-seven people in 118 locations were examined. Results The first two dimensions of the multiple correspondence analysis represented 55.3% of the variability between locations. The human capital component of the social vulnerability index showed an association with the baseline endemicity index. There was a significant reduction in positivity for schistosomes. For two rounds, for every extra 1% of initial endemicity index, the fixed effect of 13.62% increased by 0.0003%, achieving at most 15.94%. Conclusions The mass treatment intervention helped to reduce transmission of schistosomiasis in areas of high endemicity. Thus, it can be recommended that application of mass treatment should be accompanied by other control actions, such as basic sanitation, monitoring of intermediate vectors and case surveillance.

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