@Article{JICS-12-033,
author = {A. Amuche, B.M. Esiefarienrhe and I. Agaji},
title = {Accident predictive system in Benue State using artificial neural network},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {12},
number = {1},
pages = {033--040},
abstract = {Road Traffic Accident (RTA) cause serious threat to human life worldwide. Nigeria is not left out in 
this menace and in fact is ranked as one of the countries with a high number of RTA cases. This is alarming and a 
preventive measure is to be taken to avoid or reduce RTAs in the country. In this work, a system is developed to 
predict road accidents in Benue state  using Artificial Neural Network (ANN)  model. The road characteristics as 
well  as  environmental  factors  are  used  as  parameters.  Data  of  RTA  from  2008  to  2014  was  collected  from  the 
Federal Road safety Commission for predictions. The predictions will help policy makers as well as Federal Road 
Safety Commission to put in place measures to prevent occurrence of RTAs. The underlying database that store 
the RTA data was created using MYSQL relational database. The software was written using JAVA programming 
language and neuroph for the predictions.   
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22496.html}
}