Knowledge discovery from road traffic accident data in Ethiopia: Data quality, ensembling and trend analysis for improving road safety

摘要

Descriptive analyses of the magnitude and situation of road safety in general and road accidents in particular isimportant, but understanding of data quality, factors related with dangerous situations and different interesting patterns in a datais of even greater importance. Under the umbrella of an information architecture research for road safety in developing countries,the objective of this machine learning experimental research is to explore data quality issues, analyze trends and predict the role ofroad users on possible injury risks. The research employed TreeNet, Classification and Adaptive Regression Trees (CART),Random Forest (RF) and hybrid ensemble approach. To identify relevant patterns and illustrate the performance of the techniquesfor the road safety domain, road accident data collected from Addis Ababa Traffic Office is exposed to several analyses. Empiricalresults illustrate that data quality is a major problem that needs architectural guideline and the prototype models could classifyaccidents with promising accuracy. In addition an ensemble technique proves to be better in terms of predictive accuracy in thedomain under study.