@article{ref1,
title="Bayesian network-based framework for cost-implication assessment of road traffic collisions",
journal="International journal of intelligent transportation systems research",
year="2021",
author="Makaba, Tebogo and Doorsamy, Wesley and Paul, Babu Sena",
volume="19",
number="1",
pages="240-253",
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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="1348-8503",
doi="10.1007/s13177-020-00242-1",
url="http://dx.doi.org/10.1007/s13177-020-00242-1"
}