Motorcycle accident cause factors and identification of countermeasures. Volume 2 : appendix/supplemental data

Hurt, Hugh H.; Ouellet, J. V.; Thom, David R. · 1981 · ROSA P / United States. Department of Transportation. National Highway Traffic Safety Administration

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This document serves as Volume II (Appendix/Supplemental Data) of a comprehensive study titled "Motorcycle Accident Cause Factors and Identification of Countermeasures," conducted by the Traffic Safety Center at the University of Southern California for the National Highway Traffic Safety Administration. The research addresses the critical need to identify specific cause factors in motorcycle accidents to develop effective countermeasures for accident and injury prevention. The study was motivated by the necessity to move beyond simple traffic reports and gather detailed, on-scene data regarding environmental, vehicle, and human factors to accurately define the population at risk and determine which factors are over-represented in accident scenarios. The methodology involved a rigorous dual-data collection approach. Researchers conducted on-scene, in-depth investigations of 900 motorcycle accidents, collecting comprehensive data on environmental conditions, vehicle mechanics, and rider/passenger characteristics. Simultaneously, they analyzed 3,600 traffic accident reports from the same study area. To establish a baseline for risk, exposure data were collected at 505 accident sites under identical time-of-day, day-of-week, and environmental conditions. This allowed for a comparative analysis between the accident population and the general riding population. The provided text details the specific terminology used (e.g., distinguishing between "Street OEM," "Chopper," and "Enduro" motorcycles; defining helmet types; and categorizing motorcycle motions like "hobble," "weave," and "slide-out") and presents the extensive field data collection forms used to record variables such as road surface conditions, traffic control visibility, vehicle modifications, tire conditions, and injury severity metrics. While this volume primarily contains the raw data structures, definitions, and supplemental tables rather than the synthesized findings of Volume I, it establishes the framework for the study’s conclusions. The data collection instruments reveal a focus on granular details, including specific injury regions (head/neck vs. somatic), the effectiveness of protection systems like helmets, and the mechanical state of the motorcycle (e.g., brake condition, tire tread depth, suspension type). The study utilizes specific injury severity measures, such as the sum of squares of Abbreviated Injury Scale scores for different body regions, to correlate specific accident factors with injury outcomes. The significance of this work lies in its contribution to traffic safety policy and engineering. By identifying cause factors and relating them to the effectiveness of safety equipment, the research provides an evidence base for countermeasures aimed at reducing motorcycle fatalities and injuries. The detailed taxonomy of motorcycle types, modifications, and accident dynamics allows for targeted interventions, such as improving helmet standards, addressing specific vehicle design flaws, or modifying road infrastructure to mitigate identified risks. This appendix ensures the transparency and reproducibility of the main report’s findings by documenting the precise methods and definitions used in the analysis.

Key finding

The study collected and synthesized comprehensive data from 900 on-scene motorcycle accident investigations and 3,600 traffic accident reports to identify cause factors and evaluate safety countermeasures.

Methodology

on_road

Sample size: 900

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• motorcycle crash typology
• helmet protective
• motorcyclist skill
• vru crash typology
• incidence prevalence
• motorcycle conspicuity

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: crash risk outcomes