ArticlePloS one2021
Examining Bayesian network modeling in identification of dangerous driving behavior.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Triglyceride-glucose index at ICU admission predicts hospital mortality in patients with acute coronary syndrome concomitant sepsis: a Bayesian network analysis of retrospective multicenter cohort study.Frontiers in nutrition · 2026Article
- Pathways from insulin resistance to incident cardiovascular disease: a Bayesian network analysis.Cardiovascular diabetology · 2024Article
- Recurring SARS-CoV-2 variants: an update on post-pandemic, co-infections and immune response.Nanotheranostics · 2024Review
- A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus.BMC medical research methodology · 2023Article
- The identification and prediction of frailty based on Bayesian network analysis in a community-dwelling older population.BMC geriatrics · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traffic safety problems are still very serious and human factor is the one of most important factors affecting traffic crashes. Taking Next Generation Simulation (NGSIM) data as the research object, this study defines six control indicators and uses principal component analysis and K-means++ clustering methods to get the driving style of different drivers. Then use the Bayesian Networks Toolbox (BNT) and MCMC algorithm to realize the structure learning of Bayesian network. and parameter learning was completed through Netica software. Finally, the vehicle-based traffic crash risk model was created to conduct sensitivity analysis, posterior probability inference, and simulation data was used to detect the feasibility of the model. The results show that the Bayesian network modeling can not only express the relationship between the crash risk and various driving behaviors, but also dig out the inherent relationship between different influencing factors and investigate the causes of driving risks. The results will be beneficial to accurately identify and prevent risky driving behavior.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.