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Crash sequence based risk matrix for motorcycle crashes.

Kun-Feng Wu1, Lekshmi Sasidharan2, Craig P Thor3

  • 1Department of Transportation and Logistics Management, National Chiao Tung University, Taiwan.

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|April 9, 2018
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Summary
This summary is machine-generated.

This study introduces a new method to prioritize motorcycle crash types by analyzing crash sequences and injury severity. Identifying the most frequent and severe motorcycle accidents helps in developing targeted safety measures.

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Area of Science:

  • Road safety
  • Traffic accident analysis
  • Injury prevention

Background:

  • Motorcycle and powered-two-wheeler (PTW) crashes are a significant safety concern.
  • Prioritizing interventions for PTW crashes remains controversial.
  • A need exists to identify the most frequent and severe crash types for effective mitigation.

Purpose of the Study:

  • To develop and demonstrate a novel approach for prioritizing PTW crash causation sequences based on injury severity.
  • To inform the application of targeted mitigating countermeasures for motorcycle accidents.
  • To identify the most frequent and severe motorcycle crash sequences.

Main Methods:

  • Constructed a crash sequence-based risk matrix using data from the National Automotive Sampling System - General Estimating System (NASS-GES).
  • Formulated crash sequences using critical event, crash types, and sequence of events.
  • Estimated a mixed-effects (ME) model to adjust crash severity levels for contributing factors.

Main Results:

  • Identified the top five most frequent and severe motorcycle crash sequences, accounting for 23% of all crashes studied.
  • These top sequences include run-off-road, cross-median, left-turn oncoming, intersection crossing/turning, and side-impact crashes.
  • Factors influencing crash severity include helmet use, road geometry, alcohol consumption, road conditions, and lighting.

Conclusions:

  • The developed risk matrix effectively prioritizes motorcycle crash sequences by considering both frequency and severity.
  • Findings provide a data-driven basis for allocating resources towards the most critical PTW safety issues.
  • Targeting specific crash sequences and addressing contributing factors can enhance motorcycle rider safety.