Triangulated Safety Risk Report

This interactive report synthesizes consumer complaints with corroborating communications from manufacturers and regulators to provide a comprehensive view of potential vehicle safety risks.

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Analysis Overview

The accompanying exploratory analysis (Report ID DS-EDA-2024-001) profiles high-risk vehicles using the JSON data powering this dashboard. It triangulates consumer complaints, capped counts of injuries and deaths, and corroborating manufacturer communications to surface two complementary rankings: one emphasizing catastrophic outcomes and one highlighting chronic defect patterns.

Source: Automotive Safety Risk Analysis Report, Finalized October 17, 2024.

Interpreting the Rankings

  • Severity queue: Newer vehicles with relatively few incidents but high counts of injuries/deaths. These demand immediate review, even if complaint volume is low.
  • Volume queue: Older, mass-market models with persistent mechanical issues. Thousands of complaints surface long-term reliability risks.
  • Complaint volume alone is not a proxy for danger; statistical analysis found no strong correlation between total complaints and the severity score.

Data Considerations

  • Dataset generated September 10, 2025; presumed aggregation of NHTSA-style complaints, communications, and incident reports.
  • Each record covers one vehicle model-year with nested complaint metrics and communication summaries.
  • Manufacturer communications vary in quality—some are technical, others administrative boilerplate, which can mask true defect signals.
  • Counts are unnormalized; interpreting risk rates requires additional data (fleet size, miles driven).

Key Findings From Exploratory Analysis

Use these takeaways alongside the interactive rankings to prioritize investigative work.

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  • A dual-queue triage strategy is recommended: severity-ranked vehicles for urgent review, volume-ranked vehicles for trend monitoring.
  • Component patterns differ by queue. Volume-ranked vehicles frequently cite POWER TRAIN and STEERING defects in communications, while severity-ranked vehicles lack a consistent component signal.
  • High-severity leaders (e.g., 2022 Tesla Model Y) illustrate how a handful of catastrophic incidents can elevate risk even amid low complaint counts.
  • Data is observational; treat findings as investigative leads rather than evidence of causality.
Dataset Schema Snapshot

Fields are surfaced directly from triangulated_safety_risk_report.json. Use this glossary when mapping dashboard metrics to further analyses.

Field Type Description Source Nest
rank Integer Vehicle position within its severity or volume list. Top level
vehicle String Concatenated year, make, and model label. Top level
year Integer Model year captured for the record. Top level
make String Vehicle manufacturer (OEM). Top level
model String Specific model designation. Top level
complaint_metrics.total_complaints Integer Aggregate consumer complaints linked to the vehicle. complaint_metrics
complaint_metrics.total_injuries_capped Integer Capped count of injury incidents used in severity scoring. complaint_metrics
complaint_metrics.total_deaths_capped Integer Capped count of fatal incidents used in severity scoring. complaint_metrics
complaint_metrics.severity_score Integer Weighted severity value (injuries + 5× deaths) used for ranking. complaint_metrics
corroborating_communications[] Array Supporting manufacturer communications related to the vehicle. Top level
Rank ↕ Vehicle ↕ Complaints ↕ Injuries ↕ Deaths ↕ Severity Score ↕