Yearly Traffic Safety Analysis

129 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Humboldt County recorded 129 total crashes, a 4.9% increase from the 123 crashes documented in 2024. While total collisions saw a modest rise, the most notable year-over-year shift was a 27.8% increase in the number of people injured, which grew from 36 to 46. The number of fatalities decreased from 3 in the prior period to 2 in the current period.

129

▲ 4.9%was 123

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

46

▲ 27.8%was 36

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall crash trend in Humboldt County is upward, with total crashes increasing by 4.9% from 123 in 2024 to 129 in 2025. This increase was accompanied by a 27.8% rise in injuries (from 36 to 46), even as fatalities decreased from 3 to 2. The data indicates a higher number of non-fatal injury crashes in the current period compared to the prior year.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 3↓-33.3%

46

Motorists Injured

Prior: 35↑31.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal distribution of crashes shifted between the two periods. The peak day for collisions moved from Friday (28 crashes) in 2024 to Wednesday (25 crashes) in 2025. Similarly, the single hour with the most crashes shifted from 4 p.m. in the prior year (14 crashes) to 5 p.m. in the current year (12 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity profile of crashes intensified year-over-year. Although the number of fatal crashes remained constant at 2, the proportion of all crashes resulting in some form of injury increased from 22.0% in 2024 to 29.4% in 2025. This was driven by the appearance of 3 serious injury crashes in the current period, a category with no crashes in the prior year, and an increase in possible injury crashes from 7 to 15.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.6%
→0.0%prior 2
Serious Injury3serious injury crashes2.3%
Minor Injury20minor injury crashes15.5%
→0.0%prior 20
Possible Injury15possible injury crashes11.6%
↑114.3%prior 7
No Injury89no injury crashes69%
↓-5.3%prior 94

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the top contributing factor in both periods, increasing in count from 30 to 33. A significant change was observed in crashes attributed to 'Lost Control,' which more than doubled from 4 incidents in 2024 to 10 in 2025. Crashes involving 'Followed too close' also rose from 7 to 10. Conversely, incidents where 'Driving too fast for conditions' was a factor decreased from 8 to 5.

Officer-Reported Primary Contributing Cause

Animal33 (25.6%)↑10.0%prior 30
FTYROW: From stop sign10 (7.8%)↑25.0%prior 8
Followed too close10 (7.8%)↑42.9%prior 7
Lost Control10 (7.8%)
Operating vehicle in an reckless, erratic, careless, negligent manner7 (5.4%)
Other (explain in narrative): Other6 (4.7%)↑20.0%prior 5
Driving too fast for conditions5 (3.9%)↓-37.5%prior 8
Ran off road - left5 (3.9%)↓-28.6%prior 7
Ran off road - straight5 (3.9%)
FTYROW: From yield sign4 (3.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring on dry roads decreased from 61.8% in 2024 to 54.3% in 2025. Concurrently, the number of crashes on adverse road surfaces such as ice, snow, or wet pavement increased from 20 to 25. The distribution of crashes by lighting conditions and weather remained largely stable, with 'Daylight' and 'Clear' being the most common conditions in both years.

Weather

Clear69 (71.9%)
↓-2.8%prior 71
Cloudy17 (17.7%)
↑6.3%prior 16
Blowing Snow2 (2.1%)
Severe Winds2 (2.1%)
Sleet, hail2 (2.1%)
Snow1 (1.0%)
Blowing sand, soil, dirt1 (1.0%)
Fog, smoke, smog1 (1.0%)
Rain1 (1.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight68 (70.8%)
↓-4.2%prior 71
Dark - roadway not lighted16 (16.7%)
↑23.1%prior 13
Dark - roadway lighted6 (6.3%)
↓-40.0%prior 10
Dawn2 (2.1%)
Dusk2 (2.1%)
Dark - unknown roadway lighting2 (2.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry70 (73.7%)
↓-7.9%prior 76
Ice/frost12 (12.6%)
↑33.3%prior 9
Snow6 (6.3%)
↑20.0%prior 5
Wet5 (5.3%)
→0.0%prior 5
Slush1 (1.1%)
Gravel1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes were consistent, with Chevrolet (33 vehicles) and Ford (26 vehicles) leading in 2025. GMC's involvement saw a notable increase, rising from 11 vehicles in 2024 to 17 in 2025. Regarding persons involved, the 55-64 age group saw its representation increase by 60.9%, from 23 individuals in the prior year to 37 in the current year.

Top Vehicle Makes (198 vehicles)

1
CHEV33 (16.7%)
↓-8.3%prior 36
2
FORD26 (13.1%)
→0.0%prior 26
3
GMC17 (8.6%)
↑54.5%prior 11
4
DODG10 (5.1%)
↓-16.7%prior 12
5
KIA9 (4.5%)
6
BUIC9 (4.5%)
→0.0%prior 9
7
RAM8 (4%)
↑14.3%prior 7
8
CHRY7 (3.5%)
→0.0%prior 7
9
TOYO6 (3%)
10
CHEVROLET6 (3%)
↓-33.3%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

14 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (107 persons with recorded sex)

Male71 (66.4%)
↑4.4%prior 68
Female36 (33.6%)
↓-32.1%prior 53

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: ArcGIS Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2025-01-01 through 2025-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 129
  • Total persons involved: 202
  • Total vehicles involved: 198

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "iowa, IA Crash Intelligence Report: 2025." Published September 9, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2025-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

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