Development of a Naturalistic Observer-Based Rating of Near-Crash Severity in Naturalistic Driving Data

The analysis of safety-critical events, including crashes and near-crashes, from naturalistic driving studies has proven extraordinarily useful in guiding transportation safety policies, transportation technology, and transportation infrastructure. Near-crashes, which are much more common than crashes, have the potential to answer many research questions. However, they are difficult to define, and their severity is difficult to rate. By definition, there is no impact to measure in a near-crash and therefore no injury or property damage to assess. Near-crashes cover a range of scenarios, and perceptions of severity can vary greatly depending on the person experiencing or perceiving them. From a research perspective, this variability makes near-crashes challenging. Severe near-crashes may be considered most similar to crashes and serve as better surrogates than less severe near-crashes, but less severe near-crashes are still very different from “normal” driving and are still relevant to policy, technology, and infrastructure development research. In this effort, an observer-based, naturalistic near-crash severity rating protocol was developed and tested to help researchers produce near-crash event data effectively and reduce associated variability. Goals included producing a protocol that can (1) produce consistent and meaningful ratings, (2) be incorporated effectively and efficiently into the standard primary event assessment, (3) be implemented by trained data reductionists with access to video and basic kinematic data charts, (4) be applied without complex models, calculations, or statistical modeling, and (5) mirror the existing crash severity scale in implementation and conceptualization. A key concept in this work was that of conflict urgency. There is no clear answer about how urgency can or should be observed or measured in naturalistic data, especially in non-crash scenarios. It is clear, however, that the concepts of collision imminence (a sense of conflict timing) and potential crash severity (related to possible damage and injury) are important factors. Thus, an additional goal was to achieve a balance between actual kinematics, predictive outcomes, and perceived subjective risks. Operational definitions, associated research protocols, and reference guides were developed for four levels of near-crash severity ranging from Critical Severity to Lower Severity. These are documented in the appendices. At their core, the definitions are based on objective metrics such as relative speed, time-to-collision, and type of conflict, but with room for subjective assessments. An iterative approach was used in the development of these definitions, and this included assessments to evaluate interrater reliability. Results indicated that reference materials and training improve interrater reliability.

Publication Details

Journal
VTechWorks (Virginia Tech)
Published
2024-09-04
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
0.00

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article

Development of a Naturalistic Observer-Based Rating of Near-Crash Severity in Naturalistic Driving Data

VTechWorks (Virginia Tech)
Traffic and Road Safety
article

Development of a Naturalistic Observer-Based Rating of Near-Crash Severity in Naturalistic Driving Data

article en

Abstract

The analysis of safety-critical events, including crashes and near-crashes, from naturalistic driving studies has proven extraordinarily useful in guiding transportation safety policies, transportation technology, and transportation infrastructure. Near-crashes, which are much more common than crashes, have the potential to answer many research questions. However, they are difficult to define, and their severity is difficult to rate. By definition, there is no impact to measure in a near-crash and therefore no injury or property damage to assess. Near-crashes cover a range of scenarios, and perceptions of severity can vary greatly depending on the person experiencing or perceiving them. From a research perspective, this variability makes near-crashes challenging. Severe near-crashes may be considered most similar to crashes and serve as better surrogates than less severe near-crashes, but less severe near-crashes are still very different from “normal” driving and are still relevant to policy, technology, and infrastructure development research. In this effort, an observer-based, naturalistic near-crash severity rating protocol was developed and tested to help researchers produce near-crash event data effectively and reduce associated variability. Goals included producing a protocol that can (1) produce consistent and meaningful ratings, (2) be incorporated effectively and efficiently into the standard primary event assessment, (3) be implemented by trained data reductionists with access to video and basic kinematic data charts, (4) be applied without complex models, calculations, or statistical modeling, and (5) mirror the existing crash severity scale in implementation and conceptualization. A key concept in this work was that of conflict urgency. There is no clear answer about how urgency can or should be observed or measured in naturalistic data, especially in non-crash scenarios. It is clear, however, that the concepts of collision imminence (a sense of conflict timing) and potential crash severity (related to possible damage and injury) are important factors. Thus, an additional goal was to achieve a balance between actual kinematics, predictive outcomes, and perceived subjective risks. Operational definitions, associated research protocols, and reference guides were developed for four levels of near-crash severity ranging from Critical Severity to Lower Severity. These are documented in the appendices. At their core, the definitions are based on objective metrics such as relative speed, time-to-collision, and type of conflict, but with room for subjective assessments. An iterative approach was used in the development of these definitions, and this included assessments to evaluate interrater reliability. Results indicated that reference materials and training improve interrater reliability.

VTechWorks (Virginia Tech)
U.S. Department of Transportation, General Motors Corporation, Virginia Department of Transportation
Industry, innovation and infrastructure
Openalex Percentile: Top 52%
Traffic and Road Safety
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