scholarly journals Analysis of Crossing Behavior and Violations of Electric Bikers at Signalized Intersections

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Tianpei Tang ◽  
Hua Wang ◽  
Jie Ma ◽  
Xizhao Zhou
Keyword(s):  
Traffic Management ◽  
Red Light ◽  
Binary Logistic Regression ◽  
Signalized Intersections ◽  
Influential Factors ◽  
Binary Logistic Regression Model ◽  
Site Type ◽  
Demographic Attributes ◽  
Signal Time ◽  
Significant Factors

This paper focuses on investigating electric bikers’ (e-bikers) crossing behavior and violations based on survey data of 3,126 e-bikers collected at signalized intersections in Nantong, China. We first explore e-bikers’ characteristics of late crossing, incomplete crossing, and violating crossing behaviors by frequency analysis and duration distribution, and examine a few influential factors for e-bikers’ red-light running (RLR) behavior, including site type, crossing length and traffic signal countdown timers (TSCTs). E-bikers’ RLR behavior is further divided into three categories, namely GR near-violations, RR violations, and RG violations. Second, we use a binary logistic regression model to identify the relationship between e-bikers’ RLR behavior and potential influential factors, including demographic attributes, movement information, and infrastructure conditions. We not only make regression analysis for respective violation type, but also carry out an integrated regression of a census of all three types of violations. Some insightful findings are revealed: (i) the green signal time and site type are the most significant factors to GR near-violations, but with little impact on the other two violation types; (ii) the waiting time, waiting position, passing cars and crossing length exert considerable impact on RR violations; (iii) for RG violations, TSCTs, leading violators and gender are the most significant factors; (iv) it is also unveiled that site type, green signal time and TSCTs have negligible impact on the whole violations regardless of the violation types. Thus, it is more meaningful to investigate the impacts of these variables on e-bikers’ RLR behavior according to different violation types; otherwise, the potential relationship between some crucial factors and e-bikers’ RLR behavior might be ignored. These findings would help to improve intersection crossing safety for traffic management.

scholarly journals The Effects of Picking Up Primary School Pupils on Surrounding Street’s Traffic: A Case Study in Hanoi

2020 ◽  
Vol 14 (1) ◽  
pp. 237-250
Author(s):  
Dinh Hiep ◽  
Vu V. Huy ◽  
Teppei Kato ◽  
Aya Kojima ◽  
Hisashi Kubota
Keyword(s):  
Primary School ◽  
Traffic Safety ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Primary Schools ◽  
Traffic Accidents ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Factors Affecting ◽  
Almost All

Introduction: One of the significant characteristics of schools in Vietnam is that almost all parents send their children to school and/or pick up their children from school using private vehicles (motorcycles). The parents usually stop and park their vehicle on streets outside the school gates, which can lead to serious congestion and increases the likelihood of traffic accidents. Methods: The objective of this study is to find out factors affecting the picking up of pupils at primary school by evaluating the typical primary schools in Hanoi city. A binary logistic regression model was used to determine factors that influence the decision of picking up pupils and the waiting duration of parents. The behavior of motorcyclists during the process of picking up pupils at the primary school gate has been identified and analyzed in detail by the Kinovea software. Results and Discussion: The study showed that, on the way back home, almost all parents use motorbikes (89.15%) to pick up their children. During their waiting time (8.48 minutes in average), they made a lot of illegal parking actions on the street there by, causing a lot of “cognitive” errors and “crash” points surrounding in front of the primary school entrance gate. Risky picking-up behaviors were significantly observed, i.e. picking-up on opposite side of the school, making a U-turn, backing-up dangerously, parking on the middle of street, and parking on the street next to sidewalk). Conclusion: Based on the analyzed results, several traffic management measures have been suggested to enhance traffic safety and reduce traffic congestion in front of school gates. In addition, the results of the study will provide a useful reference for policymakers and authorities.

scholarly journals FENOMENA DAN DETERMINAN KESEMPATAN ANAK MASUK DUNIA KERJA

2013 ◽  
Vol 2 (1) ◽  
pp. 49
Author(s):  
Selli Nelonda
Keyword(s):  
Labor Market ◽  
Human Resources ◽  
Logistic Regression Model ◽  
Development Process ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Number Of Children ◽  
Geographic Factor ◽  
Food Expenditure ◽  
Significant Factors

In most countries including Indonesia, children are very valuable future human resources in development process; they are the future of a country. For these reasons all stakeholders have the equal obligation to promote opportunity for the children to get their basic needs for their better future. But, the reality is not always as good as it should be, for some reasons, there are many children still enter the labor market than being at school for their education. Using 2009 Susenas data with binary logistic regression model, this research find that parents education, parents marital status, parent jobs sector, economic sector activity, number of children in a household, share of food expenditure and geographic factor are the significant factors to determine the probability a child entering the labor market.

scholarly journals Gender Differences in Healthcare-Seeking Behaviors in China’s Ageing Population: An Empirical Study of Shanghai

2020 ◽  
Author(s):  
Luxue Liu ◽  
Chunlan Wang ◽  
Shangguang Yang ◽  
Kevin Lo ◽  
Xi Yang
Keyword(s):  
Gender Differences ◽  
Elderly Population ◽  
Binary Logistic Regression ◽  
The Elderly ◽  
Influential Factors ◽  
Ageing Population ◽  
Binary Logistic Regression Model ◽  
Healthcare Seeking ◽  
Factors Influencing ◽  
Tertiary Hospitals

Abstract Background: Given the growing healthcare demands of the elderly, an improved understanding of the factors influencing their healthcare-seeking behaviors is necessary to enhance the effectiveness of the current healthcare system. This study empirically investigates the factors influencing the elderly population’s healthcare-seeking behaviors in Shanghai, China, from a gender differences perspective.Methods: We combine both quantitative and qualitative analyses by constructing a binary logistic regression model and identifying the relevant influential factors based on the following types of dimensions: individual, family, and cultural.Results: The results indicate that gender differences exist in the elderly population with common diseases but not in those with severe diseases, and that the female elderly are more likely to choose non-tertiary hospitals than males. In addition, age, marital status, employment status, income, health conditions, medical insurance, family support, and social capital are found to be critical influencing factors.Conclusions: The findings provide a better understanding of the elderly population’s healthcare demands and can be used to promote healthcare equality.

Nature and determinants of migration: Insights from NSSO data in three states of India

2021 ◽  
Vol 18 (3) ◽  
pp. 261-280
Author(s):  
Ruchi Singh ◽  
Uday Salunkhe ◽  
Vaishali Vivek Patil
Keyword(s):  
West Bengal ◽  
Rural Households ◽  
Uttar Pradesh ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Monthly Expenditure ◽  
Level Data ◽  
Determining Factors ◽  
Internal Migrants ◽  
Significant Factors

The exodus of men is the most opted strategy adopted by rural households to diversify income and reduce risks associated with agriculture. Migration plays a very important role in reducing the liquidity and credit constraints of rural households in India. Data shows there are roughly 30 per cent internal migrants in the country. Despite the existence of studies on patterns and determinants of migration in various out-migrating states such as Uttar Pradesh, Bihar, Kerala etc., literature on the same in States with high in-migration are scarce. This article makes an attempt to comprehensively look into patterns and determinants of in-migration in three states. We have used the unit level data on migration (NSSO 64th Round) and tabulated it with suitable statistical techniques. To have a better understanding of determinants of in-migration in the states of Maharashtra, Delhi and West Bengal, a binary logistic regression model have been employed. We found that the majority of male migrants in these states is from within the state and but from different districts. Another interesting finding is that determining factors for migration vary across states. Social group, education, age, monthly expenditure are significant factors in determining migration decisions.

scholarly journals Factors influencing early initiation of breastfeeding among mothers in Rajshahi district, Bangladesh

2020 ◽  
Author(s):  
Ummay Ayesha ◽  
Abu Sayed Md. Al M ◽  
Md. Nurul Islam ◽  
Md. Ripter Hossain ◽  
Samme Amena Tasmia ◽  
...  
Keyword(s):  
Binary Logistic Regression ◽  
Influential Factors ◽  
Immune Defense ◽  
Early Initiation ◽  
Binary Logistic Regression Model ◽  
Chi Square ◽  
Early Initiation Of Breastfeeding ◽  
Level Ii ◽  
Chi Square Test ◽  
Initiation Of Breastfeeding

Abstract Background: Early initiation of breastfeeding (EIBF) act as the first immunization for the newborns, fortify their immune defense system and eventually reduce the mortality rate. This study aimed to identify the influential factors of EIBF among mothers in Rajshahi district, Bangladesh.Methods: A total number of 421 mothers living in Rajshahi district who had at least one child aged 6-24 months were considered as sample. Mothers were selected using multistage random sampling. This study was conducted from January to March, 2019. The EIBF was measured by a principal question, “Did you provide your breast milk to your newborns within one hour after delivery?” Frequency distribution and Chi-square test and multivariable binary logistic regression model were utilized in this study for getting prevalence and influential factors of EIBF respectively. Results: This study revealed that the prevalence of EIBF among mothers in Rajshahi district was 88.4%. Multivariable logistic model provided eight influential factors of EIBF: (i) husbands’ education level, (ii) husbands’ occupation, (iii) family monthly income, (iv) mothers’ age, (v) mothers’ BMI, (vi) place of delivery, (vii) getting pregnancy with planning, and (viii) mothers taking advice regarding the benefit of breastfeeding during their pregnancy. Conclusions: This study identified several modifiable influential factors of EIBF. The customs, culture and other characteristics are almost the same across the country. These factors could be considered to increase the rate of EIBF among mothers in Bangladesh.

scholarly journals Factors influencing early initiation of breastfeeding among mothers in Rajshahi district, Bangladesh

2020 ◽  
Author(s):  
Ummay Ayesha ◽  
Abu Sayed Md. Al M ◽  
Md. Nurul Islam ◽  
Md. Ripter Hossain ◽  
Samme Amena Tasmia ◽  
...  
Keyword(s):  
Breast Milk ◽  
Binary Logistic Regression ◽  
Influential Factors ◽  
Early Initiation ◽  
Binary Logistic Regression Model ◽  
Chi Square ◽  
Early Initiation Of Breastfeeding ◽  
Protective Antibodies ◽  
Chi Square Test ◽  
Initiation Of Breastfeeding

Abstract Background: Early initiation of breastfeeding (EIBF) is referred to as providing breast milk to the newborn within one hour of birth. EIBF ensures that the infant receives the colostrum containing highly protective antibodies. This study aimed to identify the influential factors of EIBF among mothers in Rajshahi district, Bangladesh. Methods: The data was collected from mothers living in Rajshahi district who had at least one child aged 6-24 months. This study was conducted from January to March, 2019. The EIBF was measured by a principal question, “Did you provide your breast milk to your infant within one hour after delivery?” Frequency distribution and Chi-square test and multivariable binary logistic regression model were utilized in this study for getting prevalence and influential factors of EIBF respectively. Results: This study revealed that the prevalence of EIBF among mothers in Rajshahi district was 88.4%. Multivariable logistic model provided eight influential factors of EIBF: (i) place of delivery, (ii) family monthly income, (iii) husbands’ education level, (iv) mothers’ nutritional status, (v) mothers’ age, (vi) husbands’ occupation, (vii) getting pregnancy with planning, and (viii) mothers taking advice regarding the benefit of breastfeeding during their pregnancy. Conclusions: This study identified several modifiable influential factors of EIBF. The customs, culture and other characteristics are almost the same across the country. These factors could be considered to increase the rate of EIBF among mothers in Bangladesh.

scholarly journals Factors influencing early initiation of breastfeeding among mothers in Rajshahi district, Bangladesh

2020 ◽  
Author(s):  
Ummay Ayesha ◽  
Abu Sayed Md. Al M ◽  
Md. Nurul Islam ◽  
Md. Ripter Hossain ◽  
Samme Amena Tasmia ◽  
...  
Keyword(s):  
Breast Milk ◽  
Logistic Model ◽  
Binary Logistic Regression ◽  
Influential Factors ◽  
Early Initiation ◽  
Binary Logistic Regression Model ◽  
Chi Square ◽  
Place Of Delivery ◽  
Early Initiation Of Breastfeeding ◽  
Initiation Of Breastfeeding

Abstract Background Early initiation of breastfeeding (EIBF) is referred as providing the breast milk to the newborn within one hour of birth which ensures that the infant receives the colostrum. The aim of this study was to identify the risk factors associated with early initiation of breastfeeding among mothers in Rajshahi district, Bangladesh. Methods The data was collected from mothers living in Rajshahi district who had at least one child aged 6–24 months from January to March, 2019. The EIBF was measured by a question to mother, “did you provide your breast milk to your infant within one hour after delivered? Frequency distribution, Chi-square and multivariable binary logistic regression model were utilized in this study for getting prevalence and influential factors of EIBF. Results This study revealed that the prevalence of EIBF was 88.4%. Logistic model provided that place of delivery, family monthly income, husbands’ education level, mothers’ nutritional status, mothers’ age, husbands’ occupation, getting pregnancy with planning, mothers taking advice regarding the benefit of breastfeeding during their pregnancy were the influential factors of EIBF among mothers in Rajshahi district, Bangladesh. Conclusions This study identified several modifiable influential factors of EIBF. The customs, culture and other characteristics are almost same across the country in Bangladesh. These factors could be considered to increase the rate of EIBF among mothers in Bangladesh.

Are the Enclaves on the Same Boat? An Investigation on the Development Indicators of Dahagram–Angarpota of Bangladesh

2020 ◽  
pp. 097152312094731
Author(s):  
Farjana Misu ◽  
Mst. Asma Khatun ◽  
Mohammad Amirul Islam
Keyword(s):  
Food Security ◽  
National Level ◽  
Binary Logistic Regression ◽  
Wealth Index ◽  
Influential Factors ◽  
Policy Recommendations ◽  
Indian Territory ◽  
Binary Logistic Regression Model ◽  
Development Indicators ◽  
Demographic Indicators

Enclaves are the most distressed areas in the world where the ray of development hardly reaches. This study aims to evaluate the progress in the development in Dahagram–Angarpota enclave of Bangladesh after the initiation of 24-h open corridor access through the Indian territory in 2011. The study reveals that after 24-h open access through the corridor, the enclave achieved substantial positive changes in all the five indicators, namely, economic, social and infrastructural, technological, health and demographic indicators. However, the achievement in the four influential factors of development such as income, wealth, poverty and food security are still below the national level. Binary logistic regression model identified a positive change in income–wealth index of households through the changes in corridor access, education, electricity supply, number of livestock and involvement with micro credit. In the process of raising income–wealth, achieving food security and alleviating poverty, this study has suggested some policy recommendations that would guide different development programmes in the area.

scholarly journals Factors Shaping Consumers Intention in Purchasing Life Insurance Policy in Bangladesh: A Binary Logistic Analysis

2021 ◽  
pp. 21-36
Author(s):  
Golam Ramij
Keyword(s):  
Life Insurance ◽  
Insurance Coverage ◽  
Binary Logistic Regression ◽  
Sampling Technique ◽  
Positive Influence ◽  
Jel Classification ◽  
Influential Factors ◽  
Insurance Policy ◽  
Binary Logistic Regression Model ◽  
Logistic Analysis

Abstract This paper investigates the demographic and socio-economic influential factors that work as a motivational tool for purchasing life insurance policies by the eligible consumers of Bangladesh. Using convenience sampling technique, responses of three hundred seventy-eight persons have been analyzed using this study's binary logistic regression model. This study found that demographic variables like age, education, and gender have no significant impact on life insurance policy buying decisions. On the contrary, the number of dependents in a family, nature of work, and monthly income positively impact the policy purchase decision. This study also reveals that insurance literacy has a positive influence, but acquired information about insurance policy negatively impacts consumers' purchasing decisions. In addition to that, the study's findings also show that people usually purchase a life insurance policy for tax benefit purposes instead of investment purposes. The outcomes of the study will be helpful to the insurers and policymakers to find out the major areas of improvement to bring more people under life insurance coverage, and for prospective consumers, it will guide them to understand the benefits of having a life insurance policy. JEL classification numbers: C83, C93, H55, I13. Keywords: Life insurance, Logistic model, Forecasting, Tax benefits, Agent.

Credit constraints and Rural Migration: Evidence from Six Villages in Uttar Pradesh

2018 ◽  
Vol 15 (3) ◽  
pp. 389-398
Author(s):  
Ruchi Singh
Keyword(s):  
Credit Constraints ◽  
Uttar Pradesh ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Male Migration ◽  
Rural Economies ◽  
And Migration ◽  
The Relationship ◽  
The Empirical Analysis

Rural economies in developing countries are often characterized by credit constraints. Although few attempts have been made to understand the trends and patterns of male out-migration from Uttar Pradesh (UP), there is dearth of literature on the linkage between credit accessibility and male migration in rural Uttar Pradesh. The present study tries to fill this gap. The objective of this study is to assess the role of credit accessibility in determining rural male migration. A primary survey of 370 households was conducted in six villages of Jaunpur district in Uttar Pradesh. Simple statistical tools and a binary logistic regression model were used for analyzing the data. The result of the empirical analysis shows that various sources of credit and accessibility to them play a very important role in male migration in rural Uttar Pradesh. The study also found that the relationship between credit constraints and migration varies across various social groups in UP.

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