International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 6, No. 2, 8 Jan. 2014
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Traffic Accident Analysis Using Decision Trees and Neural Networks
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Author(s)
Olutayo V.A
1,*
Eludire A. A.
1
Index Terms
Traffic Accident Data Mining, Accident Causes Prediction and Sensitivity Analysis, Performance Comparison
Abstract
This work employed Artificial Neural Networks and Decision Trees data analysis techniques to discover new knowledge from historical data about accidents in one of Nigeria’s busiest roads in order to reduce carnage on our highways. Data of accidents records on the first 40 kilometres from Ibadan to Lagos were collected from Nigeria Road Safety Corps. The data were organized into continuous and categorical data. The continuous data were analysed using Artificial Neural Networks technique and the categorical data were also analysed using Decision Trees technique .Sensitivity analysis was performed and irrelevant inputs were eliminated. The performance measures used to determine the performance of the techniques include Mean Absolute Error (MAE), Confusion Matrix, Accuracy Rate, True Positive, False Positive and Percentage correctly classified instances. Experimental results reveal that, between the machines learning paradigms considered, Decision Tree approach outperformed the Artificial Neural Network with a lower error rate and higher accuracy rate. Our research analysis also shows that, the three most important causes of accident are Tyre burst, loss of control and over speeding.
Cite This Paper
Olutayo V.A, Eludire A.A, "Traffic Accident Analysis Using Decision Trees and Neural Networks", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.2, pp.22-28, 2014. DOI:10.5815/ijitcs.2014.02.03
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