Mining Road Traffic Accident Data to Improve Safety: Role of Road-Related Factors on Accident Severity in Ethiopia

Road traffic accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury; Ethiopia in particular experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this work, we applied data mining technologies to link recorded road characteristics to accident severity in Ethiopia, and developed a set of rules that could be used by the Ethiopian Traffic Agency to improve safety.

[1]  R Raeside,et al.  An investigation into the relationships between area social characteristics and road accident casualties. , 1997, Accident; analysis and prevention.

[2]  L Mussone,et al.  An analysis of urban collisions using an artificial intelligence model. , 1999, Accident; analysis and prevention.

[3]  P J Ossenbruggen,et al.  Roadway safety in rural and small urbanized areas. , 2001, Accident; analysis and prevention.

[4]  S Y Sohn,et al.  Pattern recognition for road traffic accident severity in Korea , 2001, Ergonomics.

[5]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[6]  Wing-Gun Wong,et al.  An algorithm for assessing the risk of traffic accident. , 2002, Journal of safety research.

[7]  Bruce N. Janson,et al.  Diagnostic Methodology for the Detection of Safety Problems at Intersections , 2002 .

[8]  So Young Sohn,et al.  Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea , 2003 .

[9]  A. Abraham,et al.  RULE MINING AND CLASSIFICATION OF ROAD TRAFFIC ACCIDENTS USING ADAPTIVE REGRESSION TREES , 2005 .

[10]  Li-Yen Chang,et al.  Data mining of tree-based models to analyze freeway accident frequency. , 2005, Journal of safety research.

[11]  Ajith Abraham,et al.  Traffic Accident Analysis Using Machine Learning Paradigms , 2005, Informatica.

[12]  Li-Yen Chang,et al.  Analysis of traffic injury severity: an application of non-parametric classification tree techniques. , 2006, Accident; analysis and prevention.

[13]  Jennifer T. Wong,et al.  Comparison of Methodology Approach to Identify Causal Factors of Accident Severity , 2008 .

[14]  Getnet Mossie.  Applying Data Mining to Identify Determinant Factors of Drivers and Vehicles in Support of Reducing and Controlling Road Traffic Accident: In the Case of Addis Ababa City , 2009 .