@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} }