Traffic Accident Analysis Using Decision Trees and Neural Networks

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Author(s)

Olutayo V.A 1,* Eludire A. A. 1

1. Computer Science Department, Joseph Ayo Babalola University, Ikeji-Arakeji, Nigeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2014.02.03

Received: 21 Apr. 2013 / Revised: 5 Aug. 2013 / Accepted: 24 Sep. 2013 / Published: 8 Jan. 2014

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

Reference

[1]Olutayo, V.A. (2011); Comparison of different data mining techniques performance in knowledge discovery from road accident database. M.Sc. Thesis, Department of computer science, University of Ibadan, Nigeria.

[2]Abdelwahab, H. T. & Abdel-Aty, M. A. Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections. Transportation Research Record 1746, Paper No. 01-2234.

[3]Bedard, M., Guyatt, G. H., Stones, M. J., & Hireds, J. P., The Independent Contribution of Driver, Crash, and Vehicle Characteristics to Driver Fatalities. Accident analysis and Prevention, Vol. 34, 2002, pp. 717-727.

[4]Buzeman, D. G., Viano, D. C., & Lovsund, P., Car Occupant Safety in Frontal Crashes: A Parameter Study of Vehicle Mass, Impact Speed, and Inherent Vehicle Protection. Accident Analysis and Prevention, Vol. 30, No. 6, pp. 713-722, 1998.

[5]Dia, H., & Rose, G., Development and Evaluation of Neural Network Freeway Incident Detection Models Using Field Data. Transportation Research C, Vol. 5, No. 5, 1997, pp. 313-331.

[6]Evanco, W.M., the Potential Impact of Rural Mayday Systems on Vehicular Crash Fatalities. Accident Analysis and Prevention, Vol. 31, 1999, pp. 455-462.

[7]Kim, K., Nitz, L., Richardson, J., & Li, L., Personal and Behavioural Predictors of Automobile Crash and Injury Severity. Accident Analysis and Prevention, Vol. 27, No. 4, 1995, pp. 469-481.

[8]Kweon, Y. J., & Kockelman, D. M., Overall Injury Risk to Different Drivers: Combining Exposure, Frequency, and Severity Models. Accident Analysis and Prevention, Vol. 35, 2003, pp. 441-450.

[9]Shankar, V., Mannering, F., & Barfield, W., Statistical Analysis of Accident Severity on Rural Freeways. Accident Analysis and Prevention, Vol. 28, No. 3, 1996, pp.391-401.

[10]Ossiander, E. M., & Cummings, P., Freeway speed limits and Traffic Fatalities in Washington State. Accident Analysis and Prevention, Vol. 34, 2002, pp. 13-18.

[11]Martin, P. G., Crandall, J. R., & Pilkey, W. D., Injury Trends of Passenger Car Drivers In the USA. Accident Analysis and Prevention, Vol. 32, 2000, pp. 541-557.

[12]Mayhew, D. R., Ferguson, S. A., Desmond, K. J., & Simpson, G. M., Trends In Fatal Crashes Involving Female Drivers, 1975-1998. Accident Analysis and Prevention, Vol. 35, 2003, pp. 407-415.

[13]Mussone, L., Ferrari, A., & Oneta, M., An analysis of urban collisions using an artificial intelligence model. Accident Analysis and Prevention, Vol. 31, 1999, pp. 705-718.

[14]Yang, W.T., Chen, H. C., & Brown, D. B., Detecting Safer Driving Patterns by a Neural Network Approach. ANNIE ’99 for the Proceedings of Smart Engineering System Design Neural Network, Evolutionary Programming, Complex Systems and Data Mining, Vol. 9, pp. 839-844, Nov. 1999.

[15]Sohn, S. Y., & Lee, S. H., Data Fusion, Ensemble and Clustering to Improve the Classification Accuracy for the Severity of Road Traffic Accidents in Korea. Safety Science, Vol. 4, issue1, February 2003, pp. 1-14.

[16]Tavris, D. R., Kuhn, E. M, & Layde, P. M., Age and Gender Patterns In Motor Vehicle Crash injuries: Importance of Type of Crash and Occupant Role. Accident Analysis and Prevention, Vol. 33, 2001, pp. 167-172. 

[17]Kweon, Y. J., & Kockelman, D. M., Overall Injury Risk to Different Drivers: Combining Exposure, Frequency, and Severity Models. Accident Analysis and Prevention, Vol. 35, 2003, pp. 441-450.

[18]Zembowicz, R. & Zytkow, J. M., 1996. From Contingency Tables to Various Forms of Knowledge in Database. Advances in knowledge Discovery and Data Mining, editors, Fayyad, U. M. et al., AAAI Press/The MIT Press, pp.329-349.

[19]Akomolafe, O.P. (2004); predicting possibilities of Road Accidents occurring, using Neural Network. M. Sc. Thesis, Department of Computer Science, University of Ibadan.