Modeling of Inter-Vehicle Accident Prevention and Control System Using Rule-Based Integrated Machine Learning Technique

Abstract

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

1 Nwanze D.E.2 Eze Irene F.3 Isizoh A.N.

  1. Department of Computer Science & Maths, Novena University, Ogume, Delta State, Nigeria
  2. Department of Computer Science, Chukwuemeka Odimegwu Ojukwu University, Uli, Anambra State, Nigeria
  3. Department of Electronic & Computer Engineering, Nnamdi Azikiwe University, Awka, Nigeria

IRJIET, Volume 7, Issue 4, April 2023 pp. 174-187

doi.org/10.47001/IRJIET/2023.704028

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