An Artificial Neural Network Model for Road Accident Prediction: a Case Study of a Developing Country

dc.authorid Misra, Sanjay/0000-0002-3556-9331
dc.authorid Fernandez Sanz, Luis/0000-0003-0778-0073
dc.authorscopusid 35264573100
dc.authorscopusid 56962766700
dc.authorscopusid 6506263707
dc.authorscopusid 25630384100
dc.authorwosid OGWUELEKA, Toochukwu/ACR-2124-2022
dc.authorwosid Fernandez-Sanz, Luis/J-4895-2012
dc.authorwosid Misra, Sanjay/K-2203-2014
dc.contributor.author Ogwueleka, Francisca Nonyelum
dc.contributor.author Misra, Sanjay
dc.contributor.author Ogwueleka, Toochukwu Chibueze
dc.contributor.author Fernandez-Sanz, L.
dc.contributor.other Computer Engineering
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-10-06T10:57:07Z
dc.date.available 2024-10-06T10:57:07Z
dc.date.issued 2014
dc.department Atılım University en_US
dc.department-temp [Ogwueleka, Francisca Nonyelum] Fed Univ Wukari, Dept Comp Sci, Wukari, Taraba State, Nigeria; [Misra, Sanjay] Atilim Univ, Dept Comp Engn, Ankara 06830, Turkey; [Ogwueleka, Toochukwu Chibueze] Univ Abuja, Dept Civil Engn, Abuja, Nigeria; [Fernandez-Sanz, L.] Univ Alcala, Alcala De Henares 28801, Madrid, Spain en_US
dc.description Misra, Sanjay/0000-0002-3556-9331; Fernandez Sanz, Luis/0000-0003-0778-0073 en_US
dc.description.abstract Road traffic accidents (RTA) are one of the major root causes of the unnatural loses of human beings all over the world. Although the rates of RTAs are decreasing in most developed countries, this is not the case in developing countries. The increase in the number of vehicles and inefficient drivers on the road, as well as to the poor conditions and maintenance of the roads, are responsible for this crisis in developing countries. In this paper, we produce a design of an Artificial Neural Network (ANN) model for the analysis and prediction of accident rates in a developing country. We apply the most recent (1998 to 2010) data to our model. In the design, the number of vehicles, accidents, and population were selected and used as model parameters. The sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The performance evaluation of the model signified that the ANN model is better than other statistical methods in use. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.citationcount 20
dc.identifier.endpage 197 en_US
dc.identifier.issn 1785-8860
dc.identifier.issue 5 en_US
dc.identifier.scopus 2-s2.0-84901746322
dc.identifier.scopusquality Q1
dc.identifier.startpage 177 en_US
dc.identifier.uri https://hdl.handle.net/20.500.14411/8674
dc.identifier.volume 11 en_US
dc.identifier.wos WOS:000340689800011
dc.identifier.wosquality Q3
dc.institutionauthor Mısra, Sanjay
dc.institutionauthor Mısra, Sanjay
dc.language.iso en en_US
dc.publisher Budapest Tech en_US
dc.relation.ispartof Acta Polytechnica Hungarica en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 31
dc.subject Artificial neural network en_US
dc.subject road en_US
dc.subject accident en_US
dc.subject linear function en_US
dc.subject back propagation en_US
dc.subject vehicles en_US
dc.title An Artificial Neural Network Model for Road Accident Prediction: a Case Study of a Developing Country en_US
dc.type Article en_US
dc.wos.citedbyCount 19
dspace.entity.type Publication
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