Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 3, May 2015, Pages: 178-184
Received: May 5, 2015; Accepted: May 13, 2015; Published: May 27, 2015
Views 4847      Downloads 158
Authors
Julius Nyerere Odhiambo, Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya
Anthony Kibira Wanjoya, Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya
Anthony Gichuhi Waititu, Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya
Article Tools
Follow on us
Abstract
Road Traffic Accident (RTA) injuries, is a neglected cause of death and disability in Nairobi County. Nairobi County has the highest number of injury rates in Kenya, notably in the active age group of (15-29) years that constitutes approximately 40% of its population. This signifies the importance of properly analyzing traffic accident data and predicting injuries, not only to explore the underlying causes of RTA injuries but also to initiate appropriate safety and policy measures in the County. Thus the study modeled RTA injuries that occurred from 2002 to 2014 in Nairobi County using the Artificial Neural Networks (ANN). ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to predict future trends. However the use of ANN in accidents analysis was found to be relatively new and rare and thus the negative binomial regression approach was utilized as the study’s baseline model. The empirical study results indicated that the ANN model outperformed the negative binomial model in its overall performance.
Keywords
Road Traffic Accidents, Injuries, Artificial Neural Networks, Negative Binomial, Nairobi
To cite this article
Julius Nyerere Odhiambo, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu, Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 3, 2015, pp. 178-184. doi: 10.11648/j.ajtas.20150403.24
References
[1]
Abdel-Aty, M. A. and H. T. Abdelwahab (2004). Predicting injury severity levels in traffic crashes: a modeling comparison. Journal of transportation engineering 130 (2), 204–210.
[2]
Abdelwahab, H. T. and M. A. Abdel-Aty (2001). Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board 1746 (1), 6–13.
[3]
Akomolafe, D. T., F. Adekayode, J. Gbadeyan, and T. Ibiyemi (2009). Enhancing road monitoring and safety through the use of geospatial technology. International Journal of Physical Sciences 4 (5), 343–348.
[4]
Bayata, H. F., F. Hattatoglu, and N. Karsli (2011). Modeling of monthly traffic accidents with the artificial neural network method. Int J Phys Sci 6, 244–54.
[5]
Bedard, M., G. H. Guyatt, M. J. Stones, and J. P. Hirdes (2002). The independent contribution of driver, crash, and vehicle characteristics to driver fatalities. Accident Analysis & Prevention 34 (6), 717–727.
[6]
Cameron, A. C. and P. K. Trivedi (2013). Regression analysis of count data, Volume 53. Cambridge university press.
[7]
Evanco, W. M. (1999). The potential impact of rural mayday systems on vehicular crash fatalities. Accident Analysis & Prevention 31 (5), 455–462.
[8]
Gaber, S., F. (2010).Analysis and Assessment of Accident Characteristics: Case Study of Dhofar Governorate, Sultanate of Oman, International Journal of Traffic and Transportation Engineering, 3(4), 189-198.
[9]
IBM, Commuter Survey (2014).
[10]
Kenya Bureau of Statistics (2014). Statistical Abstract
[11]
Kenya Roads Board (2012). Annual Report
[12]
Kim, K., L. Nitz, J. Richardson, and L. Li (1995). Personal and behavioral predictors of automobile crash and injury severity. Accident Analysis & Prevention 27 (4), 469–481.
[13]
Manyara, C. G. (2013). Combating road traffic accidents in Kenya: A challenge for an emerging economy. In Proceedings from the KESSA 2013 Conference, pp. 6–7.
[14]
Mitchell, T. M. (1997). Machine learning. 1997. Burr Ridge, IL: McGraw Hill 45.
[15]
Nairobi Traffic Police Department (2014).
[16]
Nantulya, V. M. and M. R. Reich (2002). The neglected epidemic: road traffic injuries in developing countries.BMJ: British Medical Journal 324 (7346), 1139.
[17]
Nelson, M. M. and W. T. Illingworth (1991). A practical guide to neural nets, Volume 1. Addison-Wesley Reading, MA.
[18]
Odero, W. (1995). Road traffic accidents in kenya: an epidemiological appraisal. East African medical journal 72 (5),299–305.
[19]
Odero, W. (1997). Kenya: road-traffic accidents. The Lancet 349, S13.
[20]
Odero, W., P. Garner, and A. Zwi (1997). Road traffic injuries in developing countries: a comprehensive review of epidemiological studies. Tropical Medicine & International Health 2 (5), 445–460.
[21]
Odero, W., M. Khayesi, and P. Heda (2003). Road traffic injuries in kenya: magnitude, causes and status of intervention. Injury control and safety promotion 10 (1-2), 53–61.
[22]
World Health Organization. (2014). World health statistics. Retrieved from http://apps.who.int/gho/data/node.main. A989?lang=en
[23]
World Health Organization (2013). Global status report on road safety 2013: Supporting a decade of action.
[24]
WHO (World Report on Road Traffic Injury Prevention) (2012).
[25]
Yuen, K.-V. and H.-F. Lam (2006). On the complexity of artificial neural networks for smart structures monitoring. Engineering Structures 28(7), 977–984.
[26]
Zhang, G., B. E. Patuwo, and M. Y. Hu (1998). Forecasting with artificial neural networks:The state of the art. International journal of forecasting 14 (1), 35–62.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186