Relationship between fatal road traffic injury rates and Human Development Index in Iran

Abstract Background Road traffic fatalities (RTF) is the 8th cause of mortality around the world. At the end of the Decade of Action, it would be of utmost importance to revisit our knowledge on the determinants of RTF. The aim of this study is to assess factors related to RTF at global level. Methods We used road safety development index which accounts for the interactions between system, human and products to assess the RTF in 115 and 113 countries in 2013 and 2016, respectively. To analyze data, three statistical procedures (linear regression, classification and regression trees, and multivariate adaptive regression splines) were employed. Results Classification and regression trees has the best performance amongst all others followed by multivariate adaptive regression splines for 2013 and 2016 data set with an R2 around 0.83. Results show that any increase in human development index was associated with RTF reduction. Comparing RTF data of 2013 and 2016, 8 countries experienced a change of more than 30%, which demonstrated a significant relationship with GINI index (named after Corrado Gini). Considering the three components of human development index, it is revealed that education explained most of RTF variation in classification and regression trees model followed by income and life expectancy. Conclusion Policymakers should consider road collisions as a socio-economic issue. In this regard, they can make provisions to reduce RTF in the long run by focusing on enhancing the three components of human development index, mainly education. However, there is a need to investigate the causation pathway among these three components with RTF with different time-trend procedures.

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Social, economic, and legislative factors and global road traffic fatalities

10.21203/rs.3.rs-15580/v6 ◽

2020 ◽

Author(s):

Mohammad Reza Rahmanian Haghighi ◽

Mohammad Sayari ◽

Sulmaz Ghahramani ◽

Kamran Bagheri Lankarani

Keyword(s):

Human Development ◽

Human Development Index ◽

Road Traffic ◽

Regression Trees ◽

Classification And Regression Trees ◽

Multivariate Adaptive Regression Splines ◽

Development Index ◽

Adaptive Regression ◽

Classification And Regression ◽

Three Components

Abstract Background: Road traffic fatalities (RTF) is the 8th cause of mortality around the world. At the end of the Decade of Action, it would be of utmost importance to revisit our knowledge on the determinants of RTF. The aim of this study is to assess factors related to RTF at global level.Methods: We used road safety development index which accounts for the interactions between system, human and products to assess the RTF in 115 and 113 countries in 2013 and 2016, respectively. To analyze data, three statistical procedures (linear regression, classification and regression trees, and multivariate adaptive regression splines) were employed. Results: Classification and regression trees has the best performance amongst all others followed by multivariate adaptive regression splines for 2013 and 2016 data set with an R2 around 0.83. Results show that any increase in human development index was associated with RTF reduction. Comparing RTF data of 2013 and 2016, 8 countries experienced a change of more than 30%, which demonstrated a significant relationship with GINI index (named after Corrado Gini). Considering the three components of human development index, it is revealed that education explained most of RTF variation in classification and regression trees model followed by income and life expectancy. Conclusion: Policymakers should consider road collisions as a socio-economic issue. In this regard, they can make provisions to reduce RTF in the long run by focusing on enhancing the three components of human development index, mainly education. However, there is a need to investigate the causation pathway among these three components with RTF with different time-trend procedures.

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Comparison of Road Traffic Death and Injury Rates at Pedestrian Crossings in Europe

The Baltic Journal of Road and Bridge Engineering ◽

10.7250/bjrbe.2021-16.521 ◽

2021 ◽

Vol 16 (2) ◽

pp. 16-29

Author(s):

Julius Uhlmann

Keyword(s):

Great Britain ◽

Death Rate ◽

Road Traffic ◽

Injury Rate ◽

Road Traffic Injury ◽

European Countries ◽

Injury Rates ◽

The Road ◽

Traffic Injury ◽

Accident Statistics

For this study, accident statistics of 14 European countries were analysed for the number of fatalities and injuries occurring at pedestrian crossings from 2015 to 2017. The road traffic death rate (killed per 1 million inhabitants) and the road traffic injury rate (injured per 1 million inhabitants) at pedestrian crossings were calculated and compared. It was found that there are large differences between the European countries: The road traffic death rate at pedestrian crossings is the lowest in Great Britain and Germany and the highest in Poland and Lithuania. Statistical analysis showed a significant correlation between road traffic death and injury rates at pedestrian crossings.

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Regional disparities in road traffic injury rates involving elementary and junior high school children while commuting among Japan’s 47 prefectures between 2004 and 2013

Accident Analysis & Prevention ◽

10.1016/j.aap.2019.01.028 ◽

2019 ◽

Vol 125 ◽

pp. 79-84 ◽

Cited By ~ 1

Author(s):

Haruhiko Inada ◽

Jun Tomio ◽

Masao Ichikawa ◽

Shinji Nakahara

Keyword(s):

High School ◽

School Children ◽

Junior High School ◽

Junior High ◽

Road Traffic ◽

Road Traffic Injury ◽

Regional Disparities ◽

Injury Rates ◽

Traffic Injury

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Social, economic, and legislative factors and global road traffic fatalities

10.21203/rs.3.rs-15580/v3 ◽

2020 ◽

Author(s):

Mohammad Reza Rahmanian Haghighi ◽

Mohammad Sayari ◽

Sulmaz Ghahramani ◽

Kamran Bagheri Lankarani

Keyword(s):

Human Development ◽

Human Development Index ◽

Road Traffic ◽

Regression Trees ◽

Classification And Regression Trees ◽

Multivariate Adaptive Regression Splines ◽

Development Index ◽

Adaptive Regression ◽

Classification And Regression ◽

Three Components

Abstract Abstract Background: Road traffic fatalities (RTF) is the 8th cause of mortality around the world. At the end of the Decade of Action, it would be of utmost importance to revisit our knowledge on the determinants of RTF. Methods: We used road safety development index which accounts for the interactions between system, human and products to assess the RTF in 115 and 113 countries in 2013 and 2016, respectively. To analyze data, three statistical procedures (linear regression, classification and regression trees, and multivariate adaptive regression splines) were employed. Results: Classification and regression trees has the best performance amongst all others followed by multivariate adaptive regression splines for 2013 and 2016 data set with an R 2 around 0.83. Results show that any increase in human development index was associated with RTF reduction. Comparing RTF data of 2013 and 2016, 8 countries experienced a change of more than 30%, which demonstrated a significant relationship with GINI index (named after Corrado Gini). Considering the three components of human development index, it is revealed that education explained most of RTF variation in classification and regression trees model followed by income and life expectancy. Conclusion: Policymakers should consider road traffic accidents as a socio-economic issue. In this regard, they can make provisions to reduce RTF in the long run by focusing on enhancing the three components of human development index, mainly education. However, there is a need to investigate the correlation among these three components with RTF with different time-trend procedures.

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Social, economic, and legislative factors and global road traffic fatalities

10.21203/rs.3.rs-15580/v4 ◽

2020 ◽

Author(s):

Mohammad Reza Rahmanian Haghighi ◽

Mohammad Sayari ◽

Sulmaz Ghahramani ◽

Kamran Bagheri Lankarani

Keyword(s):

Human Development ◽

Human Development Index ◽

Road Traffic ◽

Regression Trees ◽

Classification And Regression Trees ◽

Multivariate Adaptive Regression Splines ◽

Development Index ◽

Adaptive Regression ◽

Classification And Regression ◽

Three Components

Abstract Background: Road traffic fatalities (RTF) is the 8th cause of mortality around the world. At the end of the Decade of Action, it would be of utmost importance to revisit our knowledge on the determinants of RTF. Methods: We used road safety development index which accounts for the interactions between system, human and products to assess the RTF in 115 and 113 countries in 2013 and 2016, respectively. To analyze data, three statistical procedures (linear regression, classification and regression trees, and multivariate adaptive regression splines) were employed. Results: Classification and regression trees has the best performance amongst all others followed by multivariate adaptive regression splines for 2013 and 2016 data set with an R2 around 0.83. Results show that any increase in human development index was associated with RTF reduction. Comparing RTF data of 2013 and 2016, 8 countries experienced a change of more than 30%, which demonstrated a significant relationship with GINI index (named after Corrado Gini). Considering the three components of human development index, it is revealed that education explained most of RTF variation in classification and regression trees model followed by income and life expectancy. Conclusion: Policymakers should consider road traffic accidents as a socio-economic issue. In this regard, they can make provisions to reduce RTF in the long run by focusing on enhancing the three components of human development index, mainly education. However, there is a need to investigate the causation pathway among these three components with RTF with different time-trend procedures.

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Social, economic, and legislative factors and global road traffic fatalities

10.21203/rs.3.rs-15580/v5 ◽

2020 ◽

Author(s):

Mohammad Reza Rahmanian Haghighi ◽

Mohammad Sayari ◽

Sulmaz Ghahramani ◽

Kamran Bagheri Lankarani

Keyword(s):

Human Development ◽

Human Development Index ◽

Road Traffic ◽

Regression Trees ◽

Classification And Regression Trees ◽

Multivariate Adaptive Regression Splines ◽

Development Index ◽

Adaptive Regression ◽

Classification And Regression ◽

Three Components

Abstract Abstract Background: Road traffic fatalities (RTF) is the 8th cause of mortality around the world. At the end of the Decade of Action, it would be of utmost importance to revisit our knowledge on the determinants of RTF. The aim of this study is to assess factors related to RTF at global level. Methods: We used road safety development index which accounts for the interactions between system, human and products to assess the RTF in 115 and 113 countries in 2013 and 2016, respectively. To analyze data, three statistical procedures (linear regression, classification and regression trees, and multivariate adaptive regression splines) were employed. Results: Classification and regression trees has the best performance amongst all others followed by multivariate adaptive regression splines for 2013 and 2016 data set with an R2 around 0.83. Results show that any increase in human development index was associated with RTF reduction. Comparing RTF data of 2013 and 2016, 8 countries experienced a change of more than 30%, which demonstrated a significant relationship with GINI index (named after Corrado Gini). Considering the three components of human development index, it is revealed that education explained most of RTF variation in classification and regression trees model followed by income and life expectancy. Conclusion: Policymakers should consider road collisions as a socio-economic issue. In this regard, they can make provisions to reduce RTF in the long run by focusing on enhancing the three components of human development index, mainly education. However, there is a need to investigate the causation pathway among these three components with RTF with different time-trend procedures.

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What Does Human Development Index Rating Mean in Terms of Individualism/Collectivism?

European Psychologist ◽

10.1027//1016-9040.8.2.97 ◽

2003 ◽

Vol 8 (2) ◽

pp. 97-100 ◽

Cited By ~ 3

Author(s):

Maria José Sotelo ◽

Luis Gimeno

Keyword(s):

Human Development ◽

Human Development Index ◽

Principal Component ◽

General Idea ◽

Development Index ◽

Low Levels ◽

The Relationship

The authors explore an alternative way of analyzing the relationship between human development and individualism. The method is based on the first principal component of Hofstede's individualism index in the Human Development Index rating domain. Results suggest that the general idea that greater wealth brings more individualism is only true for countries with high levels of development, while for middle or low levels of development the inverse is true.

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Capaian pembangunan manusia: analisis komparatif Provinsi Banten, Jawa Barat dan DKI Jakarta

Jurnal Good Governance ◽

10.32834/gg.v13i1.31 ◽

2018 ◽

Vol 13 (1) ◽

Author(s):

Adriwati Adriwati

Keyword(s):

Human Development ◽

Human Development Index ◽

Human Population ◽

Provincial Government ◽

Healthy Life ◽

Development Index ◽

Long Life ◽

The Difference ◽

The Government ◽

Development Paradigm

Human development is a development paradigm that puts human (population) as the focus and final target of all development activities, namely the achievement of control over resources (income to achieve decent living), improvement of health status (long life and healthy life) and improve education. To see the success rate of human development, UNDP publishes an indicator of Human Development Index (HDI). This study discusses the achievements of human development that have been pursued by the government. The problem analyzed in this research is the difference of human development achievement in some provincial government in Indonesia. This paper aims to compare the achievements of human development in some provincial governments seen from the achievement of human development index of each province. Research location in Banten Province, West Java and DKI Jakarta.Keywords:Human Development Index, Human Development Achievement

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