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 | |
| Publication status | Published - Apr 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being -
SDG 11 Sustainable Cities and Communities -
SDG 17 Partnerships for the Goals
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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