Research on Quantitative Method of Traffic Safety Credit Score Based on Ridge-Logistic Regression

2021 ◽  
pp. 746-752
Author(s):  
Bowen Wang ◽  
Jingsheng Wang ◽  
Benyu Wang ◽  
Runzheng Wang ◽  
Xichu Xue
Keyword(s):  
Logistic Regression ◽  
Traffic Safety ◽  
Quantitative Method ◽  
Credit Score

scholarly journals Lower Volumes, Higher Speeds: Changes to Crash Type, Timing, and Severity on Urban Roads from COVID-19 Stay-at-Home Policies

2021 ◽  
pp. 036119812110444
Author(s):  
Jonathan Stiles ◽  
Armita Kar ◽  
Jinhyung Lee ◽  
Harvey J. Miller
Keyword(s):  
Logistic Regression ◽  
Traffic Safety ◽  
Travel Demand ◽  
Multinomial Logistic Regression ◽  
High Volume ◽  
Time Of Day ◽  
Bayesian Hierarchical ◽  
Crash Data ◽  
The Road ◽  
At Home

Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio’s Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.

Are Self-Efficacy and Perceived Environmental Characteristics Determinants of Decline in Physical Activity Time?

2021 ◽  
pp. 1-8
Author(s):  
Eduarda Cristina da Costa Silva ◽  
Arthur Oliveira Barbosa ◽  
Juliana Maria da Penha Freire Silva ◽  
José Cazuza de Farias Júnior
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Data Collection ◽  
Traffic Safety ◽  
Self Efficacy ◽  
Linear Trend ◽  
Multivariable Analysis ◽  
Activity Time ◽  
Environmental Characteristics ◽  
Urban Safety

Context: This study analyzed whether self-efficacy (SE) and perceived environmental characteristics (EC) are determinants of the decline in physical activity (PA) time in adolescents. Methods: This used longitudinal observational approach, with 4 years of data collection, involving 355 adolescents (57.7% girls and 42.3% boys), average age of 11.8 years (0.1 y), from João Pessoa, Paraiba, Brazil. SE and EC were measured by scales and PA by a questionnaire. Ordinal logistic regression was used to associate SE and EC with a decline in PA. Results: There was a linear trend toward a decrease in average PA duration (58.3 [13.7] min/wk/y) and a rise in average access to places for PA (point per year) (0.6 [0.1]), urban safety (0.2 [0.1]), and traffic safety scores (0.5 [0.1]). The results of multivariable analysis indicated that SE and EC were not associated with the decline in PA. Conclusion: There was a decline in PA time, and SE and perceived EC were not determinants of this decline.

scholarly journals Sexual dimorphism of viscerocranium-A logistic model

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Lucas del Vigna Peixoto ◽  
Stefany de Lima Gomes ◽  
Ana Amelia Barbieri ◽  
Francisco Carlos Groppo ◽  
Cristhiane Martins Schmidt ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Sexual Dimorphism ◽  
Regression Model ◽  
Logistic Model ◽  
Quantitative Method ◽  
Logistic Regression Model ◽  
Sexually Dimorphic ◽  
Qualitative And Quantitative ◽  
The Right

Introduction: Sex estimates are generally based on the evaluation of qualitative and quantitative aspects of anatomic structures, however, the latter has better reproducibility and reliability. Objective: Aiming to evaluate the viscerocranium as a tool for sexual prediction and verify the possibility of creation of a logistic regression model for sexual prediction. Materials and Methods: 167 craniums - 100 male and 67 female between 22 and 85 years old from a Brazilian university´s Biobank - were evaluated. Results: It was observed that of the measures carried out were presented as sexually dimorphic, except for the measures of the right frontozygomatic point – right zygion; left frontozygomatic point – left zygion. Besides, it was possible to create a logistic regression model Sex = [logits/Sex = -24.5 + (0.20 * Nasion - Naso spine) + (0.18 * Right zygion - Naso spine)]. Conclusion: It was concluded that the measures of the viscerocranium present themselves as a factor of sexual dimorphism and the quantitative method developed was 81.4% accurate.

scholarly journals Comparison of Random Forest, Logistic Regression, and MultilayerPerceptron Methods on Classification of Bank Customer Account Closure

2021 ◽  
Vol 4 (1) ◽  
pp. 14
Author(s):  
Husna Afanyn Khoirunissa ◽  
Amanda Rizky Widyaningrum ◽  
Annisa Priliya Ayu Maharani
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Feature Selection ◽  
Random Forest ◽  
Multilayer Perceptron ◽  
Cross Validation ◽  
Early Stage ◽  
Bank Account ◽  
Credit Score

<p>The Bank is a business entity that is dealing with money, accepting deposits from customers, providing funds for each withdrawal, billing checks on the customer's orders, giving credit and or embedding the excess deposits until required for repayment. The purpose of this research is to determine the influence of age, gender, country, customer credit score, number of bank products used by the customer, and the activation of the bank members in the decision to choose to continue using the bank account that he has retained or closed the bank account. The data in this research used 10,000 respondents originating from France, Spain, and Germany. The method used is data mining with early stage preprocessing to clean data from outlier and missing value and feature selection to select important attributes. Then perform the classification using three methods, which are Random Forest, Logistic Regression, and Multilayer Perceptron. The results of this research showed that the model with Multilayer Perceptron method with 10 folds Cross Validation is the best model with 85.5373% accuracy.</p><strong>Keywords:</strong> bank customer, random forest, logistic regression, multilayer perceptron

scholarly journals MODELING THE PROBABILITY OF SPEEDING BEHAVIOUR AND ACCIDENT INVOLVEMENT USING BINARY LOGISTIC REGRESSION IN EAST JAVA PROVINCE

2020 ◽  
Vol 2 (3) ◽  
pp. 149
Author(s):  
Willy Kriswardhana ◽  
Sonya Sulistyono ◽  
Iin Ervina ◽  
Dadang Supriyanto ◽  
Nunung Nuring Hayati ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Traffic Safety ◽  
High Speed ◽  
Probability Model ◽  
Binary Logistic Regression ◽  
Driving Behavior ◽  
Maximum Speed ◽  
Speed Limit ◽  
Negative Consequences ◽  
Driving Experience

Driving at high speed has negative consequences, namely, the high number of accidents. Several factors have been considered as causes of the increasing severity of victims of traffic accidents, such as a human, vehicle, and environmental factors. The risky driving behavior factor is a factor that needs to be considered in traffic safety studies. This study aims to determine the probability model of speeding behavior based on several driver characteristics and their relationship to accident involvement. This study used a binary logistic regression method to determine the probability of driving behavior exceeding the speed limit and accident involvement. The results showed that the younger a person is, the higher the probability of breaking the maximum speed limit. Furthermore, driving experience also shows a similar trend, where the longer the driving experience of someone, the less likely it is to be involved in an accident. Directions for further research are also presented. Berkendara dengan kecepatan tinggi mempunyai konsekuensi negatif, yaitu tingginya angka kecelakaan. Beberapa faktor telah dipertimbangkan sebagai penyebab dari peningkatan tingkat keparahan korban kecelakaan lalulintas. Faktor tersebut seperti faktor manusia, kendaraan, dan lingkungan. Faktor perilaku berkendara yang berbahaya, menjadi faktor yang perlu diperhatikan dalam kajian keselamatan lalulintas. Penelitian ini bertujuan untuk mengetahui model probabilitas pada perilaku speeding berdasarkan beberapa karakteristik pengendara, serta hubungannya dengan keterlibatan kecelakaan. Penelitian ini menggunakan metode regresi logistik biner untuk mengetahui probabilitas perilaku berkendara melebihi batas kecepatan dan keterlibatan kecelakaan. Hasil penelitian menunjukkan bahwa semakin muda usia seseorang, maka semakin tinggi probabilitasnya dalam melanggar batas kecepatan maksimum. Lebih lanjut diperlihatkan bahwa pengalaman mengemudi juga menunjukkan tren yang serupa. Pengalaman mengemudi seseroang, yang lebih lama akan memperkecil kemungkinan dalam keterlibatan kecelakaan. Arahan untuk penelitian selanjutnya juga ditampilkan.

Crash Hazard Comparison between Rural and Urban Road Segments Based on GIS and Logistic Regression Technology

2012 ◽  
Vol 178-181 ◽  
pp. 1635-1640
Author(s):  
Bin Wang ◽  
Xue Dong Yan ◽  
Mei Wu An ◽  
Cui Ping Zhang ◽  
Lu Ma
Keyword(s):  
Logistic Regression ◽  
Traffic Safety ◽  
Rural Areas ◽  
Urban Areas ◽  
Hazard Index ◽  
Visual Display ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
The Difference ◽  
Urban And Rural Areas

Traffic safety in rural and urban areas is a serious public issue worldwide. In this paper, the weighted hazard index (WHI) was adopted to describe risk distributions in rural and urban areas. At the beginning, the WHI analysis results were shown in the GIS-based maps and the visual display of the hazardous segments was illustrated by ArcGIS software, which would help policymakers to assume more targeted improvement measures. Then logistic regression is introduced to assess the difference of incidence of total crashes and incidence of the fatal/injure crashes between urban and rural areas. Based on the estimation results of logistic regression analysis, the ADT (average daily traffic) and length of segments have more evident impact on the two risk factors, namely the incidence of total crashes and incidence of fatal/injure crashes. Furthermore, the differences between rural and urban areas are obvious in total crashes and fatal/injure crashes and more specifically they are all lower in rural areas with other attributes being fixed.

scholarly journals Analysis of Loan Availability using Machine Learning Techniques

2021 ◽  
pp. 15-20
Author(s):  
Sharayu Dosalwar ◽  
Ketki Kinkar ◽  
Rahul Sannat ◽  
Dr Nitin Pise
Keyword(s):  
Logistic Regression ◽  
Performance Metrics ◽  
Predictive Analytics ◽  
Critical Role ◽  
Banking System ◽  
Primary Source ◽  
Machine Learning Techniques ◽  
Loan Default ◽  
Logistic Regression Models ◽  
Credit Score

In the banking system, banks have a variety of products to provide, but credit lines are their primary source of revenue. As a result, they will profit from the interest earned on the loans they make. Loans, or whether customers repay or default on their loans, affect a bank's profit or loss. The bank's Non-Performing Assets will be reduced by forecasting loan defaulters. As a result, further investigation into this occurrence is essential. Because precise forecasts are essential for benefit maximisation, it's crucial to analyse and compare the various methodologies. The logistic regression model is an important predictive analytics tool for detecting loan defaulters. In order to assess and forecast, data from Kaggle is acquired. Logistic Regression models were used to calculate the various performance indicators. The models are compared using performance metrics like sensitivity and specificity. In addition to checking account details (which indicate a customer's wealth), the model is significantly better because it includes variables (customer personal attributes such as age, objective, credit score, credit amount, credit period, and so on) that should be considered when correctly calculating the probability of loan default. As a result, using a logistic regression approach, the appropriate clients to target for loan issuance can be easily identified by evaluating their plausibility of loan default. The model implies that a bank should assess a creditor's other attributes, which play a critical role in credit decisions and forecasting loan defaulters, in addition to giving loans to wealthy borrowers.

scholarly journals Analysis of Traffic Accident Severity at Intersection Using Logistic Regression Model

2020 ◽  
pp. 1-9
Author(s):  
Yao Tzu Hsu ◽  
Shun Chi Chang ◽  
Tzu Hsin Hsu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Traffic Safety ◽  
Logistic Regression Model ◽  
Surface Condition ◽  
Confusion Matrix ◽  
Weather Condition ◽  
Road Surface ◽  
Vehicle Type ◽  
Accident Severity

Accident severity analysis is an important issue in the field of traffic safety study, and intersections are also locations of relatively high accident rates in the roadway network. Therefore, the main purpose of this study is to establish a prediction model of intersection severity based on the binary logistic regression model of data mining technology. The data source of intersection accident is obtained from the Taichung City Police Department in Taiwan in 2018 and there are 27461 valid samples. The dependent variable is the severity of intersection accident. The independent variables include 9 variables such as month, time of accident, weather condition, light conditions, road type, road surface condition, traffic control type, accident type and vehicle type, and are analyzed by the forward selection (Wald). The research results show that time of accident, road surface condition, accident type and vehicle type have significant effects. The confusion matrix is used to verify the reliability of the model, and the results can be used as the references for reducing the degree of accident injury at the intersection in the future.

scholarly journals Assessing Self-Reported Risky Riding Behavior of Motorcyclists at Unsignalized Intersections for Sustainable Transportation

2021 ◽  
Vol 13 (16) ◽  
pp. 9144
Author(s):  
Lee Vien Leong ◽  
Shafida Azwina Mohd Shafie ◽  
Peng Kheng Gooi ◽  
Wins Cott Goh
Keyword(s):  
Logistic Regression ◽  
Traffic Safety ◽  
Social Influences ◽  
Sustainable Transportation ◽  
The Self ◽  
Control Factors ◽  
Unsignalized Intersections ◽  
Facilitating Factors ◽  
Chi Squared ◽  
And Control

In Malaysia, as more than 50% of road collisions involve motorcyclists, the traffic safety of motorcyclists is critical and must be given priority. This study aims to understand the effects of attitudes, social influences, and control factors on the risky riding behavior of motorcyclists at unsignalized intersections in Malaysia. A motorcyclist-riding-behavior survey was conducted to collect and analyze the self-reported risky riding behaviors of motorcyclists. Three main analyses, namely, frequency and percentage, crosstabulation and test of independence (chi-squared), and logistic regression were adopted to assess the self-reported risky riding behavior and its correlation with outcomes, social influences, and factors. The obtained results show that negative outcomes (χ2 = 89.689, df = 54, p = 0.002) and negative social influences (χ2 = 32.554, df = 18, p = 0.019) are significantly associated with risky riding behavior, while control factors, inhibiting (χ2 = 66.889, df = 48, p = 0.037) and facilitating factors (χ2 = 96.705, df = 72, p = 0.028), have significant effects on risky riding behavior. A greater comprehension of motorcyclists’ risky riding behavior based on their self-reported risky riding behavior and beliefs can influence motorcyclists in making positive changes in their riding style.

scholarly journals Income Levels and Factors That Influence the Unsustainability of Cassava (Manihot Utilissima) Partnership in Lampung Province, Indonesia

2019 ◽  
Vol 6 (4) ◽  
pp. 570
Author(s):  
Wan Abbas Zakaria ◽  
Teguh Endaryanto ◽  
Lidya Sari Mas Indah ◽  
Lina Marlina ◽  
Abdul Mutolib
Keyword(s):  
Logistic Regression ◽  
Quantitative Method ◽  
Agricultural Products ◽  
Survey Method ◽  
Cash Income ◽  
Land Area ◽  
Income Levels ◽  
Cassava Production ◽  
Method Analysis

Lampung Province is the largest cassava center in Indonesia. The cassava partnership pattern developed in Lampung as an effort by companies and farmers to increase production and obtain optimal benefits for each party. Unfortunately, many partnerships failed and eventually ended in the middle. This study aims to analyze the income of partner cassava farmers and non-partner cassava farmers and the factors leading to cease of the cassava partnership in Lampung Province, Indonesia. This study was conducted using a survey method in the central locations of cassava production and tapioca agroindustry centers in Lampung Province from July to September 2018.  The number of sample farmers was 126 cassava farmers (63 farmers conducted partnerships and 63 farmers had not been in partnership). Data were analyzed by descriptive quantitative method.  Analysis of the factors that influence farmers’ decisions to do partnership was assessed using logistic regression. Research of cash income of partner cassava farmers was Rp 22,855,464.79 per ha and cash income of non-partner cassava farmers was Rp 13,819,044.20. The factors that influence the decision of cassava farmers in partnerships were land area, cassava farming experience, and farming income. Although promising, many partnership patterns of agricultural products, particularly cassava in Lampung, have discontinued (break-off partnerships). The unsustainability of cassava partnership in Lampung was caused by the violations committed by farmers by selling their crops to other companies/factories and then the companies/factories paid cheaper price compared to other companies/factories that did not have partnerships with farmers.

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