Bayesian Network-Based Framework for Cost-Implication Assessment of Road Traffic Collisions

Tebogo Makaba, Wesley Doorsamy, Babu Sena Paul

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Investigating the cost-implications of road traffic collision factors is an important endeavour that has a direct impact on the economy, transport policies, cities and nations around the world. A Bayesian network framework model was developed using real-life road traffic collision data and expert knowledge to assess the cost of road traffic collisions. Findings of this study suggest that the framework is a promising approach for assessing the cost-implications associated with road traffic collisions. Moreover, adopting this framework with other computational intelligence approaches would have a positive impact towards achieving the Sustainable Development Goals in terms of road safety.

Original languageEnglish
Pages (from-to)240-253
Number of pages14
JournalInternational Journal of Intelligent Transportation Systems Research
Volume19
Issue number1
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Bayesian network
  • Cost-implication
  • Framework
  • Road traffic collisions
  • Sensitivity analysis

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • General Neuroscience
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics

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