2021
DOI: 10.3390/en14123462
|View full text |Cite
|
Sign up to set email alerts
|

Classification Trees in the Assessment of the Road–Railway Accidents Mortality

Abstract: A special element of road safety research is accidents at the interface of the road and rail system. Due to their low share in the total number of incidents, they are not a popular subject of analyses but rather an element of collective studies, whereas the specificity of the road–rail accidents requires a separate characteristic, allowing, on the one hand, to categorize these types of incidents, and on the other, to specify the factors that affect them, along with an assessment of the strength of this impact.… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
15
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
9
1

Relationship

1
9

Authors

Journals

citations
Cited by 17 publications
(15 citation statements)
references
References 30 publications
0
15
0
Order By: Relevance
“…The predictors have the same characteristics as for regression. The XGBoost (Kozłowski 2021) classifier was used for classification. The model was also built on data from the last two years (2020-2021).…”
Section: Figure 2 Dataset After One-hot Encodingmentioning
confidence: 99%
“…The predictors have the same characteristics as for regression. The XGBoost (Kozłowski 2021) classifier was used for classification. The model was also built on data from the last two years (2020-2021).…”
Section: Figure 2 Dataset After One-hot Encodingmentioning
confidence: 99%
“…Although the extant literature on the topic is limited, the findings suggest an emerging tendency for artificial intelligence (AI) and machine learning (ML) publications in the railway domain [290]. Currently, rail projects are utilizing AI and ML to optimize the effectiveness and performance of railway systems by different means, including resources and equipment planning during the construction stage [291], delving into the causation factors of highway-rail crossing crashes [292], categorizing fatality rates for accidents [293], improving safety measures [294,295], mitigating collision risks [296], and integrating building information modeling (BIM) and ML to enhance the operation and maintenance of railway networks [297].…”
Section: Citation Burst and Trend Analysismentioning
confidence: 99%
“…Oralhan et al [18] used cox regression and life table model to analyze the traffic accident data in Kayseri, Turkey, and obtained the influencing factors of fatal accidents. Kozlowski et al [19] used an improved classification tree algorithm to classify and evaluate accident factors in road-rail intersections in the Polish region to qualify and assess them.…”
Section: Study On the Influencing Factors Of Accidentsmentioning
confidence: 99%