Application of Data Mining Technology to Support Road Traffic Accident Analysis at Addis Ababa Traffic Office
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Date
2005-06
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Addis Ababa University
Abstract
Road traffic accidents are among the top le a din g causes of deaths and injuries of various
levels. Ethiopia is one of the countries of the world experiencing highest rate of such
accident s resulting in fatalities and various levels of injuries. Addis Ababa, the capital city of
Ethiopia, takes the lion 's s hare of the risk having higher number of vehicles and traffic. And
the cost of these fatalities and injuries due to such road traffic accidents has a great impact on
the socio-economic development of a society.
This thesis reports on the finding s of a research that had the objective to build a decision
support sys tem to handle road traffic accident analysis, for Addis Ababa C it y Traffic Office.
The study focused o n injury severity levels resulting from an accident. In do in g so the aim of
this research was to assess the potential applicability of data mining technology specifically
decision tree technique to help traffic accident data analysis in decision-making process at the
traffic office.
In the thesis, the process of building a model through know ledge discovery and data mining
techniques on historical accident record data is described. Different tools and techniques are
also used for the purpose of data analysis. The methodology adopted had three basic steps
name Lydia collection , data preparation, and model building and validation. The required data
was selected and extracted from Addis Ababa Traffic Office. Then, data preparation tasks
(such as data transformation, deriving of new attributes, and handling o f missing values) were
undertaken. The final step was mo del building and validation using the selected tools and
techniques.
The decision tree Knowledge SEEKER algorithm is used in the stud y. The particular too l used
for the mo de l building was the decision tree incorporated in Knowledge STUDIO. After
successive experiments, a model that can classify accidents w ell with a better accuracy as
fatal , serious, and slight or property-damage was selected and evaluated . Experiment result s
reveal that the use o f decision tree is helpful in detecting dangerous accidents through
identifying behavioral and roadway accident patterns. The reported findings are promising,
making the proposed model a useful" tool in the decision making process. And the whole
research process can be a good input for further in-depth research.
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Information Science