Investigation and Modeling of Road Traffic Accidents at Five-Legged Roundabouts in Addis Ababa City
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Date
2018-06
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Addis Ababa University
Abstract
In present time, road traffic accident is becoming among the most threatening concerns that
prominently hamper the social, economic, and environmental conditions in Ethiopia and Africa
at large. So far, previous crash prediction models at roundabout intersections have focused
predominantly on the customary three-legged and four-legged roundabout types. This study
however aimed at investigating and modeling road traffic Accidents/crashes at five-legged
roundabouts in Addis Ababa City, Ethiopia. To this effect, the study collected necessary
quantitative and qualitative data from both primary and secondary data sources. The data were
analyzed through descriptive and inferential statistical approaches. APMs were developed
using the Poisson and Negative Binomial Regression Models for the Severe-crashes (personal
injuries & fatal crashes), and property damages only (non-injury) crashes, respectively.
Accordingly, the study revealed that the peak hour vehicle volume entering the roundabout
through the major road, ratio of diameter of the inscribed circle to diameter of the central
island, and entry half road way width have positive associations with the frequency and
occurrences of RTAs –particularly the PDO crashes at roundabouts. Furthermore, the study
finally forwarded that reducing the entry half road width on the minor leg; constructing and
maintaining bypass/detour roads; implementing advanced traffic management schemes; and
decreasing the circulatory roadway width were important points that should be taken in to
considerations so as to decrease crashes by decreasing the peak hour flows and optimizing the
circulatory road way width to increase diameter of the central island. It has to be noted that
special care should be required to consider flow parameters such as level of service and
capacity situations.
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Keywords
Road Traffic Accidents, Roundabouts, Poisson, Negative Binomial, Accident Predictive Models, Addis Ababa