Determination of the Severity of Motorcycle and Tricycle Accidents in Nigeria
Advances in Applied Sciences
Volume 5, Issue 2, June 2020, Pages: 41-48
Received: May 11, 2020; Accepted: May 27, 2020; Published: Jun. 17, 2020
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Authors
Terungwa Simon Yange, Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria
Oluoha Onyekwere, Department of Computer Science, University of Nigeria, Nsukka, Nigeria
Malik Adeiza Rufai, Department of Computer Science, Federal University, Lokoja, Lokoja, Nigeria
Charity Ojochogwu Egbunu, Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria
Onyinyechukwu Rehoboth Ogboli, Department of Computer Science, Federal University, Lokoja, Lokoja, Nigeria
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Abstract
Road traffic accidents are a very rampant issue causing injury, loss of lives and property worldwide. In this research, a system for determining the severity of motorcycle accidents in Lokoja Metropolis of Central Nigeria was developed. The research considered different areas that are highly prone to accidents in Lokoja. Although accidents cannot be totally avoided, through scientific analysis, their frequency and severity can be reduced. The methodology used in this research is Knowledge Discovery in Databases with the Decision Tree Algorithm as the soft computing technique used for analysis. Python programming language was used for the implementation. The dataset used was gotten from the Federal Road Safety Corps (FRSC) in Lokoja. After the training and testing of the dataset, we achieved an accuracy of 90.5%. The motorcycle accident severity prediction system developed could serve as a tool that can be used to cub the enormous challenges faced by FRSC in curtailing motorcycle accident.
Keywords
Severity, Motorcycle, Accident, Knowledge Discovery, Decision Tree
To cite this article
Terungwa Simon Yange, Oluoha Onyekwere, Malik Adeiza Rufai, Charity Ojochogwu Egbunu, Onyinyechukwu Rehoboth Ogboli, Determination of the Severity of Motorcycle and Tricycle Accidents in Nigeria, Advances in Applied Sciences. Vol. 5, No. 2, 2020, pp. 41-48. doi: 10.11648/j.aas.20200502.14
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Afolabi, O. J., and Kolawole, G. T. (2017). Road Traffic Crashes in Nigeria: Causes and Consequences. The International Journal of Transport & Logistics, 17 (42), 1046-1069.
[2]
Ikot, A. S., Akpan, U. U., Benson, P. J. and Etim, O. P. (2011). Motorcycle Ban and Its Economic Implications on Uyo Metropolis of Akwa Ibom State, Nigeria. International Journal of Economic Development Research and Investment, 2 (3): 32-39.
[3]
Morenikeji, O. (2012). An Analysis of Motorcycle Traffic and Crashes in Nigeria – A case study of Minna, Nigeria. Journal of Technological Research, 7 (2): 59-66.
[4]
Oluwaseyi, O. S., Edward, E., Eyinda, C. A. and Okoko, Eno. E. (2014). Performance Assessment of Motorcycle Operation, as a Means of Urban Mobility in Lokoja, Nigeria. Journal of Transportation Technologies, 4: 343-354.
[5]
Omoke, N. I., Lasebikan, O. A., Onyemaechi, N. O. and Ajali, N. (2019). Auto Tricycle Injuries and the Vulnerability of Occupants and Pedestrians in a Developing Country: A Multi-Center Study. Niger Journal of Clinical Practice, 22: 971-976.
[6]
Ohakwe, J., Iwueze, I. S., and Chikezie, D. C. (2011). Analysis of Road Traffic Accidents in Nigeria: A Case Study of Obinze/Nekede/Iheagwa Road in Imo State, Southeastern, Nigeria. Asian Journal of Applied Sciences, 4 (2), 166-175.
[7]
Akomolafe, D. T., and Olutayo, A. (2012). Using Data Mining Technique to Predict Cause of Accident Prone Locations on Highways. American Journal of Database Theory and Application, 1 (3), 26-38.
[8]
Olorunfemi, S. Edward, E. Eyinda, C. and Okoko, E. (2014). Performance Assessment of Motorcycle Operation as a Means of Urban Mobility in Lokoja, Nigeria. Journal of Transportation Technologies, 4: 343-354.
[9]
Olutayo, V. A., and Eludire, A. A. (2014). Traffic Accident Analysis Using Decision Trees and Neural Networks. I. J. Information Technology and Computer Science, 02, 22-28. DOI: 10.5815/ijitcs.2014.02.03
[10]
Yahaya, B. Z., Muhammad, L. J., Abdulganiyyu, N., Ishaq, F. S., and Atomsa, Y. (2019). An Arithmetic Mean of Information Gain and Correlation Ratio Based Decision Tree Algorithm for Accident Dataset Mining: A Case Study of Accident Dataset of Gombe- Numan- Yola Highway, Nigeria. International Journal of Advanced Science and Technology, 127, 51-58.
[11]
Wahab, L., and Jiang, H. (2019). A Comparative Study on Machine Learning Based Algorithms for Prediction of Motorcycle Crash Severity. PLOS ONE 14 (4): e0214966.
[12]
Agbonkhese, O., Yisa, G. L., Agbonkhese, E. G., Akanbi, D. O., Aka, E. O. and Mondigha, E. B. (2013). Road Traffic Accidents in Nigeria: Causes and Preventive Measures. Civil and Environmental Research, 3 (13), 2225-0514.
[13]
Dadashova, B., Ramírez, B. A., McWilliams, J. M. and Izquierdo, F. A. (2016). The Identification of Patterns of Interurban Road Accident Frequency and Severity using Road Geometry and Traffic Indicators. Transportation Research Procedia, 14: 4122 – 4129
[14]
Wang, Y. and Zhang, W. (2017). Analysis of Roadway and Environmental Factors Affecting Traffic Crash Severities. Transportation Research Procedia, 25: 2119–2125.
[15]
Islam, M. and Kanitpong, K. (2008). Identification of Factors in Road Accidents Through In-Depth Accident Analysis. IATSS Research, 32 (2): 58-67.
[16]
Wang, J., Zheng, Y., Li, X., Yu, C., Kodaka, K., and Li, K. (2015). Driving Risk Assessment using Near- Crash Database through Data Mining of Tree-Based Model. Accident Analysis and Prevention, 84: 54-64.
[17]
Naqvi, H. M. and Tiwari, G. (2017) Factors Contributing to Motorcycle Fatal Crashes on National Highways in India. Transportation Research Procedia, 25: 2084–2097.
[18]
Zhang H. (2010). Identifying and Quantifying Factors Affecting Traffic Crash Severity in Louisiana. Dissertation submitted to the Louisiana State University.
[19]
Keller, J. (2003). Analysis of Type and Severity of Traffic Crashes at Signalized Intersections Using Tree-Based Regression and Ordered Probit Models. Thesis submitted to the University of Central Florida, Orlando, FL.
[20]
Ratanavaraha, V. and Suangka, S. (2014). Impacts of Accident Severity Factors and Loss Value of Crashes on Expressways in Thailand, International Association of Traffic and Safety Science Research, 37: 130-136.
[21]
Senk, P., Ambros, J., Pokorny, P., and Striegler, R. (2012). Use of Accident Prediction Models in Identifying Hazardous Road Locations. Transaction on Transport Sciences, 5 (4). DOI: 10.2478/v10158-012-0025-0
[22]
Muhammad, L. J., Salisu, S., Yakubu, A., Malgwi, Y. M., Abdullahi, E. T., Mohammed, I. A., and Muhammad, N. A. (2017). Using Decision Tree Data Mining Algorithm to Predict the Causes of Road Traffic Accidents, its Prone Locations and Time along Kano- Wudil Highway. International Journal of Database Theory and Application, 10 (1), 197-206.
[23]
Moghaddam, F. R., Afandizadeh, S., and Ziyadi, M. (2010). Prediction of Accident Severity using Artificial Neural Networks. International Journal of Civil Engineering, 9 (1).
[24]
Li, Y., Duan, Y., Kang, W., Li, Z., and Wang, F. Y. (2015). Traffic Flow Prediction with Big Data: A Deep Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 16 (2).
[25]
Ramachandiran, V. M., Babu, P. N. K., and Manikandan, R. (2018). Prediction of Road Accidents Severity using various Algorithms. International Journal of Pure and Applied Mathematics, 119 (12), 16663-16669.
[26]
Hazaa, M. A. S., Saad, R. M. A., and Alnaklani, M. A. (2019). Prediction of Traffic Accident Severity using Data Mining Techniques in IBB Province, Yemen. International Journal of Software Engineering and Computer Systems (IJSECS), 5 (1): 77-92.
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