Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
This
research focuses on the modeling of inter-vehicle accident prevention and
control system using rule-based integrated machine learning technique. From the
review of relevant literatures, many works have been developed as solutions for
accident detection and control system for vehicles, but despite the success,
solution has not been fully obtained which considered tricycle as a major cause
of accident, even though it has dominated the means of transport and logistics
in many developing countries, especially in Nigeria. To solve this problem,
data of tricycles were collected from the Delta State Ministry of Transport,
Asaba; and then trained with machine learning algorithm to generate the
accident detection model. The rule-based optimization was developed from the
information collected from the Federal Road Safety Corp (FRSC) on the standard
of inter vehicle distance and then used to develop the control model. The model
was implemented with Simulink and evaluated. The result when tested and
validated showed that the accident detection accuracy is 98.1%; Mean Square
Error (MSE) is 3.0512e-10 and ROC is 0.9807. When compared with other models
trained with similar data type, the result showed that the Feed Forward Neural
Network (FFNN) developed was better and more accurate with a percentage
improvement of 5.1%.
Country : Nigeria
IRJIET, Volume 7, Issue 4, April 2023 pp. 174-187