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Analysis of motorcycle accidents using association rule mining-based framework with parameter optimization and GIS
Feifeng Jiang1, Kwok Kit Richard Yuen1, Eric Wai Ming Lee1
1Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China.
Journal of Safety Research
|December 18, 2020
Summary
This study introduces an objective framework using Association Rule Mining (ARM) and GIS to identify key factors in motorcycle accidents, improving road safety analysis and policy decisions.
Area of Science:
- Road Safety
- Data Mining
- Geographic Information Systems (GIS)
Background:
- Motorcycle accident analysis is crucial for reducing fatalities.
- Existing Association Rule Mining (ARM) studies lack objective parameter setting and in-depth rule analysis.
- Spatial analysis of accident data is seldom incorporated for policy insights.
Purpose of the Study:
- To develop an objective ARM-based framework for identifying critical factors in motorcycle injury severity.
- To propose methods for objective parameter optimization and factor extraction in ARM.
- To integrate GIS for spatial analysis of accident factors and severity.
Main Methods:
- An ARM-based framework with objective parameter optimization was developed.
- Factor extraction identified individual and boosting factors from ARM rules.
- Geographic Information System (GIS) was used for spatial analysis of accident data.
Main Results:
- The framework objectively determined optimal support (0.03) and confidence (0.7) thresholds for ARM.
- Five individual and four boosting factors linked to fatal motorcycle injuries were identified.
- GIS mapping revealed accident hot spots related to fatal factors.
Conclusions:
- The proposed framework offers improved objectivity and analytical depth for ARM in accident studies.
- GIS-enabled spatial analysis provides intuitive insights for policymakers.
- The framework is adaptable for analyzing various traffic accident types.
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