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Human Injury Causing Road Traffic Accident at Debre Markos Town
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Objective: A Road traffic accident (RTA) is when in a road Vehicle collides with another Vehicle, pedestrian, animal or geographical or architectural obstacle. The RTAs can result in human injury, property damage and death. RTA result in the deaths of 1.2 million people worldwide each year and injuries about 4 times this number. The objective of this study is to identify the main causing-factors that contribute to road traffic accidents involving human injuries. Literature suggested factors considered for analysis are: Driver’s Age, Driver’s Education status, Driver’s experience, Vehicle type, Driver Vehicle Ownership, Vehicle year of service, Road type, Road division, Road condition, Problem with car, Weather condition, and accident time (day or night). Results: Among the candidate variables, Pearson Chi-Square method identified weather condition, driver’s experience, Vehicle year of service; Road division, Driver Vehicle Ownership, and accident time (day or night) as significantly associated variables. Furthermore, percentage is used to describe the magnitude of associated variable. The result from Poisson regression analyses revealed that low driver experience, high Vehicle year of service (old cars), and Road division (one way road) are the significant contributing factors for increment of human injuries accidents.
Title: Human Injury Causing Road Traffic Accident at Debre Markos Town
Description:
Objective: A Road traffic accident (RTA) is when in a road Vehicle collides with another Vehicle, pedestrian, animal or geographical or architectural obstacle.
The RTAs can result in human injury, property damage and death.
RTA result in the deaths of 1.
2 million people worldwide each year and injuries about 4 times this number.
The objective of this study is to identify the main causing-factors that contribute to road traffic accidents involving human injuries.
Literature suggested factors considered for analysis are: Driver’s Age, Driver’s Education status, Driver’s experience, Vehicle type, Driver Vehicle Ownership, Vehicle year of service, Road type, Road division, Road condition, Problem with car, Weather condition, and accident time (day or night).
Results: Among the candidate variables, Pearson Chi-Square method identified weather condition, driver’s experience, Vehicle year of service; Road division, Driver Vehicle Ownership, and accident time (day or night) as significantly associated variables.
Furthermore, percentage is used to describe the magnitude of associated variable.
The result from Poisson regression analyses revealed that low driver experience, high Vehicle year of service (old cars), and Road division (one way road) are the significant contributing factors for increment of human injuries accidents.
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