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Published on: February 25, 2013
Analyzing heterogeneous accident data from the perspective of accident occurrence
Jinn-Tsai Wong1, Yi-Shih Chung
1Institute of Traffic and Transportation, National Chiao Tung University, 4F, 114 Chung Hsiao W. Rd., Sec. 1, Taipei 100, Taiwan. jtwong@mail.nctu.edu.tw
Accident; Analysis and Prevention
|January 25, 2008
Summary
This study used rough set theory to analyze accident data, revealing that frequent accidents involve high-risk drivers, while less frequent ones are linked to road facilities. This approach offers a new way to understand accident data heterogeneity.
Area of Science:
- Traffic Safety
- Data Science
- Accident Analysis
Background:
- Accident data often exhibits heterogeneity, making analysis challenging.
- Existing clustering and classification methods have limitations in capturing this complexity.
- Understanding the underlying processes of accident occurrences is crucial for effective intervention.
Purpose of the Study:
- To investigate the heterogeneity in accident data by examining the process of accident occurrences.
- To apply rough set theory for deriving rules that explain accident outcomes.
- To group accidents based on the frequency of derived rules for deeper analysis.
Main Methods:
- Utilized rule-based classification with rough set theory to identify indispensable factors in accident outcomes.
- Derived rules reflecting the process of accident occurrences.
- Grouped accidents based on the occurring frequency of each derived rule.
Main Results:
- High-frequency accident rules were predominantly associated with drivers exhibiting high-risk characteristics.
- Low-frequency accident rules highlighted the significant role of road facilities.
- Distinct features emerged between frequently recurring and sparsely occurring accident processes.
Conclusions:
- Accident data heterogeneity is multi-faceted, extending beyond single factors like age, gender, or location.
- The proposed method, considering the accident occurrence process, provides a more comprehensive analysis of accident data heterogeneity.
- This approach offers a valuable alternative for understanding complex accident patterns.
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