{"slug":"nhtsa-recalls","title":"Recalls Data","description":"About the Data: The dataset includes recall information related to specific NHTSA campaigns. Users can filter based on characteristics like manufacturer and component. The dataset can also be filtered by recall type: tires, vehicles, car seats, and equipment. The earliest campaign data is from 1966. Maintained by the Department of Transportation.","publisher":"National Highway Traffic Safety Administration","organization_title":"Department of Transportation","landing_page":"https://data.transportation.gov/api/views/6axg-epim/rows.csv?accessType=DOWNLOAD","file_count":1,"use_cases":[{"title":"Audit fleet for outstanding recalls","example":"A logistics company finds that 12 of its delivery vans have an unresolved fuel system recall with a do-not-drive advisory, requiring immediate grounding.","how":"Match fleet vehicle makes, models, and years against the recall dataset. Prioritize any matches with do-not-drive advisories or low completion rates.","risk":true},{"title":"Screen manufacturer quality for procurement","example":"A fleet manager comparing truck manufacturers finds one brand has 3x more safety recalls per model than competitors over the past 5 years, weighted toward braking system defects.","how":"Group recalls by manufacturer and component type. Normalize by model count and production volume to create a per-manufacturer defect rate.","risk":true},{"title":"Track recall completion for compliance","example":"A rental car company discovers that a recall affecting 200,000 vehicles has only a 34% completion rate 18 months after issuance, meaning two-thirds of affected vehicles are still on the road.","how":"Filter by recall campaign and check the completion rate field. Low completion rates on high-severity defects indicate ongoing fleet risk.","risk":true},{"title":"Research defect pattern trends","example":"An automotive journalist finds that software-related recalls in autonomous driving systems increased from 3 in 2020 to 47 in 2024, reflecting the industry's rapid deployment of self-driving features.","how":"Search defect summaries for keywords related to software, sensors, or autonomous systems. Track year-over-year volume to identify emerging defect categories.","risk":false}],"enrichment":{"jurisdiction_names":["United States"],"jurisdiction_levels":["federal"],"date_range":{"start":"1966"},"date_descriptions":["Since 1966, with completion rate data from 2015 Q1 onward"],"cluster_name":"Rail Equipment Accident Reports","has_geo_fields":false,"has_contact_fields":false,"has_temporal_fields":false,"has_financial_fields":false,"has_business_data":true,"resource_version_url":"https://data.transportation.gov/api/views/6axg-epim/rows.csv?accessType=DOWNLOAD"},"schema_count":1,"schemas":[{"columns":["website","business_name"],"column_mapping":null,"sample_rows":[]}]}