Modelling Road Work Zone Crashes’ Nature and Type of Person Involved Using Multinomial Logistic Regression Article Swipe
Adriana Carvalho Pinto Vieira
,
Bertha Santos
,
Luís Picado-Santos
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su15032674
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su15032674
The sustainable development goals “Good health and well-being” and “Sustainable cities and communities” of the United Nations and World Health Organization, alert governments and researchers and raise awareness about road safety problems and the need to mitigate them. In Portugal, after the economic crisis of 2008–2013, a significant amount of road assets demand investment in maintenance and rehabilitation. The areas where these actions take place are called work zones. Considering the particularities of these areas, the proposed work aims to identify the main factors that impact the occurrence of work zones crashes. It uses the statistical technique of multinomial logistic regression, applied to official data on road crashes occurred in mainland Portugal, during the period of 2010–2015. Usually, multinomial logistic regression models are developed for crash and injury severity. In this work, the feasibility of developing predictive models for crash nature (collision, run off road and running over pedestrians) and for type of person involved in the crash (driver, passenger and pedestrian), considering only one covariate (the number of persons involved in the crash), was studied. For the two predictive models obtained, the variables road environment (urban/rural), horizontal geometric design (straight/curve), pavement grip conditions (good/bad), heavy vehicle involvement, and injury severity (fatalities, serious and slightly injuries), were identified as the preponderant factors in a universe of 230 investigated variables. Results point to an increase of work zone crash probability due to driver actions such as running straight and excessive speed for the prevailing conditions.
Related Topics
Concepts
Multinomial logistic regression
Crash
Logistic regression
Work (physics)
Transport engineering
Pedestrian
Poison control
Investment (military)
Business
Computer science
Engineering
Statistics
Environmental health
Mathematics
Medicine
Programming language
Political science
Mechanical engineering
Law
Politics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su15032674
- https://www.mdpi.com/2071-1050/15/3/2674/pdf?version=1675311743
- OA Status
- gold
- Cited By
- 12
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319015177
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319015177Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su15032674Digital Object Identifier
- Title
-
Modelling Road Work Zone Crashes’ Nature and Type of Person Involved Using Multinomial Logistic RegressionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-02Full publication date if available
- Authors
-
Adriana Carvalho Pinto Vieira, Bertha Santos, Luís Picado-SantosList of authors in order
- Landing page
-
https://doi.org/10.3390/su15032674Publisher landing page
- PDF URL
-
https://www.mdpi.com/2071-1050/15/3/2674/pdf?version=1675311743Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2071-1050/15/3/2674/pdf?version=1675311743Direct OA link when available
- Concepts
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Multinomial logistic regression, Crash, Logistic regression, Work (physics), Transport engineering, Pedestrian, Poison control, Investment (military), Business, Computer science, Engineering, Statistics, Environmental health, Mathematics, Medicine, Programming language, Political science, Mechanical engineering, Law, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 4, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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