Comparative analysis and prediction of traffic accidents in Sudan using artificial neural networks and statistical methods.

Show simple item record Ali, Galal A. Bakheit, Charles S.
dc.contributor.other Southern African Transport Conference (30th : 2011 : Pretoria, South Africa)
dc.contributor.other Transportation Research Board of the National Academies (TRB)
dc.contributor.other Minister of Transport, South Africa 2011-09-26T12:54:54Z 2011-09-26T12:54:54Z 2011-07
dc.description This paper was transferred from the original CD ROM created for this conference. The material was published using Adobe Acrobat 10.1.0 Technology. The original CD ROM was produced by Document Transformation Technologies Postal Address: PO Box 560 Irene 0062 South Africa. Tel.: +27 12 667 2074 Fax: +27 12 667 2766 E-mail: nigel@doctech URL: en_US
dc.description.abstract Paper presented at the 30th Annual Southern African Transport Conference 11-14 July 2011 "Africa on the Move", CSIR International Convention Centre, Pretoria, South Africa. en_US
dc.description.abstract Road traffic accidents (RTAs) are one of the major causes of death in Sudan, notably in the age group of 20 to 40 that constitutes 44% of the population. Fatality rate per 10,000 vehicles is one of the highest in the world, in spite of Sudan's low vehicle-per-capita ratio of 125 persons per car (average value over the last 20 years) Thus, it signifies the importance of properly analyzing traffic accident data and predicting casualties. Such studies will explore the underlying causes of RTAs and thereby develop appropriate safety measures to reduce RTA casualties. In this paper, analysis and prediction of RTAs in Sudan were undertaken using Artificial Neural Networks (ANNs). ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to predict future trends. However, the use of ANNs in the area of traffic engineering and accidents analysis is relatively new and rare. Input variables to ANN model were carefully selected through examining the strength of the correlation between the annual number of accidents and related variables such as annual population growth, gross domestic product, number of driving licenses issued annually, etc. For further validation of the model, principle component regression (PCR) technique was used to fit the same data. Both approaches attempted to model accidents using historical data on related factors, such as population, number of cars on the road and so on, covering the period from 1991 to 2009. Forecasts for the years 2005 to 2012 were made using ANNs and principle component regression method. Analysis using ANNs resulted in the best fit for the data with high R'. However, both methods provided forecasts that were very similar in values. The study showed that ANNs are more suitable for interpolation than extrapolation. Nevertheless, it demonstrates that ANNs provide a potentially powerful tool in analyzing and forecasting traffic accidents and casualties. en_US
dc.description.sponsorship CD sponsored by TRANSNET en_US
dc.format.extent 13 pages en_US
dc.format.medium PDF en_US
dc.identifier.citation Ali, GA & Bakheit, CS 2011, 'Comparative analysis and prediction of traffic accidents in Sudan using artificial neural networks and statistical methods', Paper presented to the 30th Annual Southern African Transport Conference, South Africa, 11-14 July. pp. 202-214 en_US
dc.identifier.isbn 9781920017514
dc.language.iso en en_US
dc.publisher Document Transformation Technologies en_US
dc.relation.ispartof SATC 2011
dc.rights University of Pretoria en_US
dc.subject Accident characteristics and causes en_US
dc.subject Comparative analysis en_US
dc.subject Casualties en_US
dc.subject Fatality rates en_US
dc.subject Safety measures en_US
dc.subject Road traffic accidents en_US
dc.subject Sudan en_US
dc.subject.lcsh Transportation
dc.subject.lcsh Transportation -- Africa en
dc.subject.lcsh Transportation -- Southern Africa
dc.title Comparative analysis and prediction of traffic accidents in Sudan using artificial neural networks and statistical methods. en_US
dc.type Presentation en_US

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