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

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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.

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Acknowledgments

The authors would like to gratefully acknowledge the Department of Applied Information Systems, the Institute for Intelligent Systems and the University of Johannesburg for availing resources for the study to be successful. The authors are also thankful to the Gauteng Department of Community Safety for providing knowledge and the dataset. Authors are also thankful to Dr. B Gatsheni for his useful comments.

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Authors and Affiliations

  1. Department of Applied Information Systems, University of Johannesburg, Johannesburg, South Africa

    Tebogo Makaba

  2. Institute for Intelligent Systems, University of Johannesburg, Johannesburg, South Africa

    Wesley Doorsamy & Babu Sena Paul

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  1. Tebogo Makaba
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Correspondence to Tebogo Makaba.

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Makaba, T., Doorsamy, W. & Paul, B.S. Bayesian Network-Based Framework for Cost-Implication Assessment of Road Traffic Collisions. Int. J. ITS Res. 19, 240–253 (2021). https://doi.org/10.1007/s13177-020-00242-1

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