  Precise estimation of Crash Modification Factors (CMFs) is central to
evaluating the effectiveness of various road safety treatments and prioritizing
infrastructure investment accordingly. While customized study for each
countermeasure scenario is desired, the conventional CMF estimation approaches
rely heavily on the availability of crash data at given sites. This not only
makes the estimation costly, but the results are also less transferable, since
the intrinsic similarities between different safety countermeasure scenarios
are not fully explored. Aiming to fill this gap, this study introduces a novel
knowledge-mining framework for CMF prediction. This framework delves into the
connections of existing countermeasures and reduces the reliance of CMF
estimation on crash data availability and manual data collection. Specifically,
it draws inspiration from human comprehension processes and introduces advanced
Natural Language Processing (NLP) techniques to extract intricate variations
and patterns from existing CMF knowledge. It effectively encodes unstructured
countermeasure scenarios into machine-readable representations and models the
complex relationships between scenarios and CMF values. This new data-driven
framework provides a cost-effective and adaptable solution that complements the
case-specific approaches for CMF estimation, which is particularly beneficial
when availability of crash data or time imposes constraints. Experimental
validation using real-world CMF Clearinghouse data demonstrates the
effectiveness of this new approach, which shows significant accuracy
improvements compared to baseline methods. This approach provides insights into
new possibilities of harnessing accumulated transportation knowledge in various
applications.
