Mode and Shelter Choice Planning During Evacuation: A Multinomial Logistic Regression Analysis of COVID-19-Induced Migration in India

People’s actions are always accompanied with multiple motives. How to estimate the role of the pro-environment motivation under the interference of other motivations will help us to better interpret human environmental behaviors. On the basis of classical motivation theories and travel mode choice research backgrounds, the concepts of pro-environmental and self-interested motivation were defined. Then based on survey data on 1244 urban residents in the Jiangsu Province in China, the multinomial logistic regression model was constructed to examine the effects of multiple motivations, government measures, and demographic characteristics on residents’ travel mode choice behaviors. The result indicates that compared to car use, pro-environmental motivation certainly has a significant and positive role in promoting green travel mode choices (walking, bicycling, and using public transport), but this unstable green behavior is always dominated by self-interested motivations rather than the pro-environmental motivation. In addition, the effects of gender, age, income, vehicle ownership, travel distance, and government instruments show significant differences among travel mode choices. The findings suggest that pro-environmental motivation needs to be stressed and highlighted to ensure sustainable urban transportation. However, policies aimed to only increase the public awareness of environment protection are not enough; tailored policy interventions should be targeted to specific groups having different main motivations.

How Household Roles Influence Individuals’ Travel Mode Choice under Intra-household Interactions?

In the current study, the variations of soil classes at the first and second levels of WRB (World Reference Base for soil resource) soil classification system were investigated by two machine learning including multinomial logistic regression (MLR) and random forest (RF) models in an arid floodplain which covers an area approximately 600 km2 located in Sistan region, Iran. The model’s performance was tested using 10-fold cross-validation by calculation of overall model accuracy and the kappa statistic. Three main Reference Soil Groups (RSGs) including Cambisols, Fluvisols, and Solonchaks at the first level, and 18 WRB soil groups at the second level were identified. Results showed that the overall accuracy at the first level of WRB was 53% and 49% with a kappa of 0.26 and 0.19 for MLR and RF models, respectively. At the second level of WRB, the overall accuracy was 11% and 21% with a kappa of 0 and 0.09 for MLR and RF models, respectively. Also, results showed that the MLR model had better performance (overall accuracy = 53%) at the first level of WRB, but the RF model showed better prediction (overall accuracy = 21%) at the second level of WRB. Multiresolution Valley Bottom Flatness Index (MrVBF), Normalized Difference Salinity Index (NDSI), Multiresolution of Ridge Top Flatness Index (MrRTF), convergence index, and channel network base level were among top covariates used for prediction at two levels of WRB. Results revealed the complexity of soil variations in this floodplain. Using other covariates such as soil texture and salinity maps can improve the prediction power. Increasing the size of sampling is recommended to improve the accuracy of the models in predicting the second level of WRB in this area.

Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

Sanitary sewer pipes infrastructure system being in good condition is essential for providing safe conveyance of the wastewater from homes, businesses, and industries to the wastewater treatment plants. For sanitary sewer pipes to deliver the wastewater to the treatment plants, they must be in good condition. Most of the water utilities have aged sanitary sewer pipes. Water utilities inspect sewer pipes to decide which segments of the sanitary sewer pipes need rehabilitation or replacement. The process of inspecting the sewer pipes is described as condition assessment. This condition assessment process is costly and necessitates developing a model that predicts the condition rating of sanitary sewer pipes. The objective of this study is to develop Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) models to predict sanitary sewer pipes condition rating using inspection and condition assessment data. MLR and ANN models are developed from the City of Dallas’s data. The MLR model is built using 80% of randomly selected data and validated using the remaining 20% of data. The ANN model is trained, validated, and tested. The significant physical factors influencing sanitary pipes condition rating include diameter, age, pipe material, and length. Soil type is the environmental factor that influences sanitary sewer pipes condition rating. The accuracy of the performance of the MLR and ANN is found to be 75% and 85%, respectively. This study contributes to the body of knowledge by developing models to predict sanitary sewer pipes condition rating that enables policymakers and sanitary sewer utilities managers to prioritize the sanitary sewer pipes to be rehabilitated and/or replaced.

The study of youths’ mobility-related decisions is of great significance to the nation’s future transportation planning. Although developing countries like India have a considerable share of youth, research exploring travel behavior of youth is scant. Besides, females and males are proven to have dissimilar travel patterns in developing countries, thus need to be studied separately. With this viewpoint, this study aims to analyze the factors influencing travel behavior of female students commuting by public transport in Dehradun, India. The survey employs 400 respondents whose travel characteristics, attitude towards public transport, and socio-economic characteristics data have been collected and analyzed using multinomial logistic regression model. Study findings conclude that household income, age, location factor, and attitude towards public transport significantly influence female students’ travel mode choices. Thus, this study provides useful inputs for future public transport planning and policy implications.

The aim of this study is to investigate the practical application of the Multinomial (many explanatory variables and many categories) Logistic Regression (MLR) model, which is a fundamental tool for analyzing not only for scale data but also for categorical data with many explanatory variables. This method is primarily used when there is a single nominal or ordinal response variable with multiple categories or levels. MLR analysis has various applications across disciplines such as education, social sciences, healthcare, behavioural research, and some other fields. We utilized real data from the Azadi Heart Center at the Duhok Hospital in the Duhok Governorate to assess the practical applicability of the model. The main multinomial logistic regression model was used with five explanatory variables. Extensive statistical tests were performed to confirm the suitability of this model for the dataset. Furthermore, the model underwent a validation process wherein two observations were randomly selected from the dataset, and their categorization was predicted based on the values of the explanatory variables utilized. Our results suggest that the multinomial logistic regression model provides a useful method for distinguishing between the response variable and the set of explanatory factors that makes it easier to determine the exact influence of each variable and enables predictions about how a particular instance will be classified.

A considerable number of empirical studies have explored the effects of information & communication technologies (ICT) on travel in recent years. In particular, the most attention has been paid to whether the use of ICT increases or decreases trip frequency (i.e., substitution or complementarity effects). However, the subject of whether or how travel distance and mode choice are altered by ICT (i.e., modification effects) has almost been ignored. Against this background, using data collected in Beijing, China, this paper aims to explore how purchasing intangible services (e.g., eating out at restaurants, hairdressing, and visits to zoos and movie theatres) online alters the distance and mode choice of the travel to consume these services. The results suggest that due to online purchases of intangible services, people tend to travel farther to consume these services. Consequently, 25.4% of online buyers change their travel mode choices from walking or cycling (i.e., nonmotorized modes) to public transit, private cars, or taxis (i.e., motorized modes). These findings confirm the existence of modification effects of ICT on travel. Additionally, a stepwise multinomial logistic regression model and a stepwise binomial logistic regression model are used to detect the factors influencing changes in travel distance and mode choices, respectively. The regression outcomes suggest that people who have lower living costs or feel more satisfied with online purchases are more likely to increase their travel distances and to change from nonmotorized modes to motorized modes.

Promoting pro-environmental travel modes is an important strategy for sustainable transportation. Previous studies have shown a positive relationship between environmental awareness and environmental-friendly travel modes, but very few studies have considered pro-environmental behaviour and choice of travel mode, particularly in the context of non-Western countries. This study examines the impact of pro-environmental behaviour on the choice of commuting mode in Tokyo, Beijing, Shanghai and Singapore using original survey data. We use the Multiple Indicator Multiple Cause model to construct latent variables of environmentally friendly behaviours. The multinomial logistic regression results indicate that (1) pro-environmental activities and commuting mode choice are unrelated in Tokyo and Singapore, (2) recycling and energy-savings activities are positively related to commuting by bicycle/on foot in Beijing, and (3) participants in organised pro-environmental activities are less likely to use pro-environmental commuting modes in Beijing and Shanghai. The results provide supporting evidence of the habit discontinuity hypothesis and suggest a possible substitution effect between environmentally friendly travel mode choice and other environmental activities.

Quantitative risk assessment of submersible pump components using Interval number-based Multinomial Logistic Regression(MLR) model

Action recognition by joint learning

BackgroundUsing XGBoost (XGB), this study demonstrates how flexible machine learning modelling can complement traditional statistical modelling (multinomial logistic regression) as a sensitivity analysis and predictive modelling tool in occupational health research.DesignThe study predicts welfare dependency for a cohort at 1, 3, and 5 years of follow-up using XGB and multinomial logistic regression (MLR). The models’ predictive ability is evaluated using tenfold cross-validation (internal validation) and geographical validation (semi-external validation). In addition, we calculate and graphically assess Shapley additive explanation (SHAP) values from the XGB model to examine deviation from linearity assumptions, including interactions. The study population consists of all 20–54 years old on long-term sickness absence leave due to self-reported common mental disorders (CMD) between April 26, 2010, and September 2012 in 21 (of 98) Danish municipalities that participated in the Danish Return to Work program. The total sample of 19.664 observations is split geospatially into a development set (n = 9.756) and a test set (n = 9.908).ResultsThere were no practical differences in the XGB and MLR models’ predictive ability. Industry, job skills, citizenship, unemployment insurance, gender, and period had limited importance in predicting welfare dependency in both models. On the other hand, welfare dependency history and reason for sickness absence were strong predictors. Graphical SHAP-analysis of the XGB model did not indicate substantial deviations from linearity assumptions implied by the multinomial regression model.ConclusionFlexible machine learning models like XGB can supplement traditional statistical methods like multinomial logistic regression in occupational health research by providing a benchmark for predictive performance and traditional statistical models' ability to capture important associations for a given set of predictors as well as potential violations of linearity.Trial registrationISRCTN43004323.

To generate and evaluate a new logistic regression model for the prediction of successful expectant management of first trimester miscarriage. Data were collected prospectively from women diagnosed with 1st trimester miscarriage. Clinical and ultrasonographic variables were recorded for multivariate analysis. Clinically stable women who were managed expectantly were followed up for two weeks until the outcome was established: success or failure. A multinomial logistic regression (MLR) model was developed on 186 training cases for the prediction of successful expectant management and tested prospectively on a further 126 cases. The performance of the model was evaluated using receiver operating characteristic (ROC) curve as well as in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Two thousand and forty eight consecutive first trimester women underwent TVS. Complete data from 312 (15.2%) women with miscarriage managed expectantly were included in the final analysis. The most important independent prognostic variables for the MLR model were as follows: type of miscarriage at primary scan, vaginal bleeding and maternal age. When developed retrospectively on a training data set, MLR model gave an area under the ROC curve (AUC) of 0.796. Prospective validation of MLR model on a new test data set resulted in an AUC of 0.803. We have developed and validated a new mathematical model to predict successful management of first trimester miscarriage.

To predict 3-Level version of European Quality of Life-5 Dimensions (EQ-5D-3L) questionnaire utility from the chronic obstructive pulmonary disease (COPD) assessment test (CAT), the study attempts to collect EQ-5D-3L and CAT data from COPD patients. Response mapping under a backward elimination procedure was used for EQ-5D score predictions from CAT. A multinomial logistic regression (MLR) model was used to identify the association between the score and the covariates. Afterwards, the predicted scores were transformed into the utility. The developed formula was compared with ordinary least squares (OLS) regression models and models using Mean Rank Method (MRM). The MLR models performed as well as other models according to mean absolute error (MAE) and root mean squared error (RMSE) evaluations. Besides, the overestimation for low utility patients (utility ≤ 0.6) and underestimation for near health (utility > 0.9) in the OLS method was improved through the means of the MLR model based on bubble chart analysis. In conclusion, response mapping with the MLR model led to performance comparable to the OLS and MRM models for predicting EQ-5D utility from CAT data. Additionally, the bubble charts analysis revealed that the model constructed in this study and MRM could be a better predictive model.

Bearings are critical components in rotating machinery and their failure can lead to catastrophic outcomes, including system downtime and financial losses. Accurate fault detection and prediction in bearings can significantly improve the reliability and efficiency of industrial systems. The reliable operation of rotating machinery is critically dependent on early and accurate detection of bearing faults. This research presents an intelligent fault classification and prediction framework utilizing Support Vector Machine (SVM) and Multinomial Logistic Regression (MLR) models applied to vibration signal data. The dataset consists of multiple fault categories including inner race, outer race, and ball defects under various severity levels. After preprocessing and feature extraction, both SVM and MLR models were trained and evaluated using a confusion matrix, precision, recall, and F1-score metrics. The SVM model demonstrated superior classification performance, particularly in accurately detecting complex fault patterns, achieving an overall accuracy of 96%, compared to 94% with logistic regression. Comparative analysis highlights the strengths of SVM in handling non-linear decision boundaries, while logistic regression offers simpler interpretability and faster training times. The results show that SVM provides high accuracy in detecting and classifying different types of bearing faults, making it a suitable method for real-time condition monitoring applications.