Road Traffic Crashes Severity Analysis in Nifas-Silk/Lafto Sub City, Addis Ababa, Ethiopia
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Date
2018-06
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AAU
Abstract
Road crashes are one of the main causes of deaths worldwide. The total number of road traffic deaths
has plateaued at 1.25 million per year, with the highest road traffic fatality rates in low-income
countries (WHO, 2015). The probability of crash occurring is influenced by numerous factors like
roadway Geometric characteristics, vehicle characteristics, pavement conditions and weather
conditions each of these factors contribute its own share towards occurrence of crashes.
Reducing the severity of injuries resulting from Road Traffic crashes has long been the emphasis of
highway agencies and road traffic safety policy makers. While progress can be simply measured by
the reduction in injury levels over time, insights into the effectiveness of injury-reduction
technologies, policies, and regulations require a more detailed empirical assessment of the complex
interactions that vehicle, roadway (Road Geometry) and human factors have on resulting crash-injury
severities. Over the years, researchers have used a wide range of methodological tools to assess the
impact of such factors on disaggregate-level injury-severity data, and recent methodological advances
have enabled the development of sophisticated models capable of more precisely determining the
influence of these factors.
The main objective of this study was to identify factors which affect the severity of crashes in Addis
Ababa specifically in N/lafto sub city and map the hot spot. Multinomial Logistic Regression has been
used to analyze the crash data to identify significant factors which affect the severity of crashes.
Secondary data which obtained from Addis Ababa Traffic Police Commission and the respective subcity
from July 01, 2013(G.C) to June 30, 2015(G.C). The contestant variables for the study were
driver's age, driver's sex, driver's education status, driver's experience, driver's defect, vehicle type,
vehicle ownership, Land use, Road Type, weather condition, light condition and time.
In order to make interpretation on factors affecting the injury severity of crashes in Addis Ababa
N/silk Lafto sub city, Multinomial model was used. The results indicated that driver's age, sex,
education status, experience and vehicle type were positively associated with injury severity. Therefore, the variables that were found those factors contribute to crashes severity were driver related
factors such as age, education level, experience, gender, time related factors and land use. In order to
reduce injury severity in Addis Ababa, it is important to refine those factors that affect the road traffic
crash severity. Studying how those factors contribute to crashes help concerned bodies to give
attention to the factors which were positively associated with injury severity and these findings can
serve as a base for safety measures and policies.
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Keywords
Road crashes, Geometric characteristics, vehicle characteristics, pavement conditions, MNL Model, Descriptive Analysis, Injury severity, crash severity