A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater Melbourne Article Swipe
Quynh Duong
,
Hulya Gilbert
,
Hien D. Nguyen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.aap.2024.107747
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.aap.2024.107747
The field of spatial analysis in traffic crash studies can often enhance predictive performance by addressing the inherent spatial dependence and heterogeneity in crash data. This research introduces the Geographical Support Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assess spatial variations and evaluate the influence of a wide range of factors, including traffic, infrastructure, socio-demographic, travel demand, and land use, on the incidence of total and fatal-or-serious injury (FSI) crashes across Greater Melbourne's zones. Utilizing data from the Melbourne Activity-Based Model (MABM), the study examines 50 indicators related to peak hour traffic and various commuting modes, offering a detailed analysis of the multifaceted factors affecting road safety. The study shows that active transportation modes such as walking and cycling emerge as significant indicators, reflecting a disparity in safety that heightens the vulnerability of these road users. In contrast, car commuting, while a consistent factor in crash risks, has a comparatively lower impact, pointing to an inherent imbalance in the road environment. This could be interpreted as an unequal distribution of risk and safety measures among different types of road users, where the infrastructure and policies may not adequately address the needs and vulnerabilities of pedestrians and cyclists compared to those of car drivers. Public transportation generally offers safer travel, yet associated risks near train stations and tram stops in city center areas cannot be overlooked. Tram stops profoundly affect total crashes in these areas, while intersection counts more significantly impact FSI crashes in the broader metropolitan area. The study also uncovers the contrasting roles of land use mix in influencing FSI versus total crashes. The proposed framework presents an approach for dynamically extracting distance matrices of varying sizes tailored to the specific dataset, providing a fresh method to incorporate spatial impacts into the development of machine learning models. Additionally, the framework extends a feature selection technique to enhance machine learning models that typically lack comprehensive feature selection capabilities.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.aap.2024.107747
- OA Status
- hybrid
- Cited By
- 4
- References
- 80
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- OpenAlex ID
- https://openalex.org/W4401689490
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401689490Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.aap.2024.107747Digital Object Identifier
- Title
-
A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater MelbourneWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-19Full publication date if available
- Authors
-
Quynh Duong, Hulya Gilbert, Hien D. NguyenList of authors in order
- Landing page
-
https://doi.org/10.1016/j.aap.2024.107747Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.aap.2024.107747Direct OA link when available
- Concepts
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Support vector machine, Crash, Regression analysis, Poison control, Regression, Computer science, Engineering, Occupational safety and health, Transport engineering, Data mining, Machine learning, Statistics, Medicine, Medical emergency, Mathematics, Programming language, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
- References (count)
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80Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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