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.

翻译：精准估计碰撞修正系数（CMF）是评估各类道路安全措施有效性及据此优先分配基础设施投资的核心。尽管针对每种对策场景进行定制化研究是理想选择，但传统CMF估计方法严重依赖特定地点的碰撞数据可得性。这不仅导致估计成本高昂，而且由于未能充分挖掘不同安全对策场景间的内在相似性，其结果的可迁移性也较差。为填补这一空白，本研究提出了一种新颖的CMF预测知识挖掘框架。该框架深入探究现有对策间的联系，降低了CMF估计对碰撞数据可得性和人工数据收集的依赖性。具体而言，它从人类理解过程中汲取灵感，引入先进的自然语言处理（NLP）技术，从现有CMF知识中提取复杂的差异与模式。该框架能够将非结构化的对策场景有效编码为机器可读的表示形式，并建模这些场景与CMF值之间的复杂关系。这一新颖的数据驱动框架提供了一种高性价比且适应性强的解决方案，可补充针对特定案例的CMF估计方法，尤其在碰撞数据或时间受限的情况下尤为有利。基于真实世界CMF信息交换所数据的实验验证表明，该方法效果显著，与基线方法相比，其准确率有大幅提升。该研究为在各种应用场景中利用累积的交通知识提供了新的可能性。