Global Journal of Engineering Sciences (GJES)
A
Data Driven Approach to Identify the Contributing Factors of Traffic Death in
Low, Middle, and High- Income Countries
Authored by Utpal Dutta
Abstract
Road
traffic crashes have been and will continue to be one of the primary causes of
mortality all over the world. Globally, road traffic crashes have a widespread
and devastating effect on public health and the global economy. According to
The World Health Organization (WHO) [1], it is estimated that there are around
1.24 million people who die worldwide from traffic crashes. It is imperative to
find out what factors contribute to the Traffic Death Rate (TDR).
This
paper presents the finding of a study that used a data driven approach to
identify the contributing factors of TDR in three groups of countries (low,
medium, high) according to Gross National Income (GNI) per capita defined by
WHO. By Traffic Death Rate (TDR), we define it as Number of Traffic/Number of
Registered Vehicles.
This
study intends to address the following research questions:
• Do
TDRs have different patterns among the countries in three level of GNI?
• If
yes, what are the main factors impacts to the TDRs positively or negatively? At
what degree?
Based
on the finding, recommendations aiming different types of countries will be
made to make campaign to reduce TDR.
Data collection and Analysis
Three
years of (2007, 2010 and 2013) traffic death related data along with GNI of
each country are collected. The data sources include the WHO [1] and its partners;
The World Bank, the United Nation (UN), and other international organizations.
Data elements consist of number of road traffic deaths, total population, total
area, urban population, alcohol consumption, number of registered vehicles,
Education Index, Human Development Index, and GNI. Analysis variables are
derived from these elements. Kolmogorov- Smirnov test and t-test show that the
distributions of the TDR are significantly different among the three groups of
countries with different means. Thus all related variables are standardized,
and stepwise linear regression analysis was conducted within each group of
countries using TDR as the dependent variable. The independent variables
include: Registered Vehicles / Population, Registered Vehicles / Proportion of
Urban population, Income, Education Index, Human Development Index, Population
Density, Proportion of Urban population, Social Globalization, Cultural
Globalization, Alcohol Consumption, Average Drivers Ages.
The
regressions produce three distinct models presented in Table 1.
These
models show the following results which will be discussed and interpreted in
detail in the full paper:
•
Proportion of Urban Population is the only significant factor common to all
three types countries. It can be interpreted as the higher the Proportion of
Urban Population, the lower the TDR and its effect is more significant in the
low income countries.
•
Vehicles/Population has been identified a factor increasing the TDR in case of
low and medium income countries, with more severe in the low income countries.
•
Population Density and Average Drivers Age are factors negatively associated
with TDR in the medium and high income countries both with lighter effect in
high income countries.
•
Alcohol Consumption contributes to TDR in low and medium income countries.
•
Human Development Index has displayed conflicting influence between medium and
low income countries.
•
Social Globalization Index is a negatively impacted factor in medium income
countries.
Conclusion
This paper identified a set of factors that influenced traffic death in countries with different level of GNI. Some factors are common between and among countries. Most cases common variables follow similar trends other than one with conflicting trends. Based on these results, we can come up with different recommendations for different groups of countries to control the TDR. For future study, a clustering analysis will be performed to classify the counties based on more comprehensive scenario and then significant factor will be identified.
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