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

Tebogo Makaba; Wesley Doorsamy; Babu Sena Paul

doi:10.1007/s13177-020-00242-1

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

Tebogo Makaba, Wesley Doorsamy, Babu Sena Paul

Institute for Intelligent Systems

University of Johannesburg

Research output: Contribution to journal › Article › peer-review

10 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 language	English
Pages (from-to)	240-253
Number of pages	14
Journal	International Journal of Intelligent Transportation Systems Research
Volume	19
Issue number	1
DOIs	https://doi.org/10.1007/s13177-020-00242-1
Publication status	Published - 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s13177-020-00242-1

Cite this

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

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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