Enhancing Predictive Analysis of Vehicle Accident Risk: A Fuzzy-Bayesian Approach

Although delivery transport activities aim to ensure excellent customer service, risks such as accidents, property damage, and additional costs occur frequently, necessitating risk control and prevention as critical components of transport supply chain quality. This article analyzes the risk of accidents, a fundamental root cause of critical situations that can have significant economic impacts on transport companies and potentially lead to customer loss if recurring. The case study develops a fuzzy Bayesian approach to anticipate accident risks through predictive analysis by combining Bayesian networks and fuzzy logic. Results reveal a strong correlation between fatal injuries in accidents and factors related to driver and vehicle conditions. The predictive model for accident occurrence is validated through three axioms, offering insights for carriers, transport companies, and governments to minimize accidents, injuries, and costs. Moreover, the developed model provides a foundation for various predictive applications in freight transport and other research fields aiming to identify parameters impacting accident occurrence.

@article{Mensouri2024,
  title     = {Enhancing Predictive Analysis of Vehicle Accident Risk: A Fuzzy-Bayesian Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Houssam Mensouri and Loubna Bouhsaien and Youssra Amazou and Abdellah Azmani and Monir Azmani},
  doi       = {10.14569/IJACSA.2024.01507101},
  url       = {https://doi.org/10.14569/IJACSA.2024.01507101}
}