Bayesian Network-Based Framework for Cost-Implication Assessment of Road Traffic Collisions
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Bayesian Network-Based Framework for Cost-Implication Assessment of Road Traffic Collisions Tebogo Makaba 1 & Wesley Doorsamy 2 & Babu Sena Paul 2 Received: 13 August 2020 / Revised: 3 November 2020 / Accepted: 9 November 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Bayesian network . Cost-implication . Framework . Road traffic collisions . Sensitivity analysis
1 Introduction Road traffic collisions (RTCs) are a major global issue that have escalated to the point where road users are losing their lives on a daily basis. The high number of RTCs has negatively impacted the public health care facilities and their resources that cannot meet the demand. This remains a global concern for all the developing countries [1]. In 2018, the World Health Organization reported that an estimated 1.35 million lives were lost each year due to RTCs. The increase in RTCs consequently resulted in a high number of road injuries and fatalities that escalated and caused major public health issues globally. The World Health Organization [2] reported that nearly 20–50 million individuals suffered serious injuries of which many resulted in physical disability and rehabilitation for the victims. Also, RTCs contribute to fatality statistics globally as
* Tebogo Makaba [email protected] Wesley Doorsamy [email protected] Babu Sena Paul [email protected] 1
Department of Applied Information Systems, University of Johannesburg, Johannesburg, South Africa
2
Institute for Intelligent Systems, University of Johannesburg, Johannesburg, South Africa
the highest killer of road users; in all age groups [2]. Globally different countries have factors and complications responsible for RTCs from a literature perspective. Therefore, RTCs cause a high-cost burden to victims and; their families and have an economic impact on society. In many cases, victims die as a result of RTCs and their families may have a hard time covering funeral costs. In Africa, studies have reported that RTCs range from 0.3% to 41% rapidly increasing by day; Studies by [3–6] indicate that RTCs are a major concern when traveling on the African roads. In 2019, the RTC annual cost to the economy in South Africa (SA) was estimated at ZAR164 billion [7]. This estimate indicated that road safety issues were a major concern that required urgent attention from d
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