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Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory
1School of Civil & Environmental Engineering, Nanyang Technological University, 639798, Singapore.
A new Key Risk Indicator (KRI) framework uses hybrid metrics to assess pre-accident risks. This approach effectively predicts accident severity and identifies at-risk vehicles, locations, and times.
Area of Science:
- Road safety
- Traffic accident analysis
- Predictive modeling
Background:
- Accidents are rare and unexpected, necessitating proactive risk assessment.
- Surrogate indicators can identify risk levels and improve accident prediction.
- Existing methods may not fully capture complex pre-accident risk dynamics.
Purpose of the Study:
- To propose and develop a Key Risk Indicator (KRI) framework for pre-accident risk assessment.
- To evaluate the effectiveness of hybrid indicators in assessing risk exposures.
- To provide predictive insights for accident prevention.
Main Methods:
- Developed a Key Risk Indicator (KRI) framework using seven hybrid metrics.
- Employed a grid remapping method for data extraction from surveillance footage.
- Utilized a temporal-spatial case-control design to evaluate indicator feasibility.
- Compared Time Integrated Time-to-collision (TIT) and Crash Potential Index (CPI) for risk assessment.
Main Results:
- Time Integrated Time-to-collision (TIT) effectively identifies pre-accident risk conditions.
- Crash Potential Index (CPI) excels at pinpointing near-accident scenarios.
- The developed KRI framework provides three levels of risk severity and likelihood.
- KRI analysis successfully predicted at-risk vehicles, locations, and times.
Conclusions:
- Hierarchical indicators like KRIs offer novel insights into pre-accident risk exposures.
- The KRI framework demonstrates strong potential for enhancing accident assessment and prediction.
- Flexible threshold definitions in KRIs simplify practical application without critical impact.
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