Yearly Traffic Safety Analysis

124 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In O'Brien County, total vehicle crashes decreased by 6.1% from 132 in 2024 to 124 in 2025. The most significant year-over-year change was the reduction in traffic fatalities, which dropped from two in the prior period to zero in the current period. Concurrently, total injuries fell from 68 to 44, a 35.3% decrease.

124

▼ -6.1%was 132

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

44

▼ -35.3%was 68

Persons Injured

0

▼ -100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

Overall traffic safety trends in O'Brien County showed improvement year-over-year. The total number of crashes declined from 132 to 124, while the number of injuries recorded decreased from 68 to 44. Fatal crashes were eliminated, dropping from two in 2024 to zero in 2025.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 2↓-100.0%

44

Motorists Injured

Prior: 66↓-33.3%

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 patterns of crashes shifted between the two periods. In 2025, the peak day for crashes was Tuesday with 23 incidents, whereas in 2024, the peak was Friday with 27 incidents. The peak hour also changed, moving from the 5 p.m. hour (13 crashes) in 2024 to the 9 a.m. hour (12 crashes) in 2025.

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

Crash severity decreased notably from 2024 to 2025. Fatal crashes, which accounted for 1.5% of incidents in 2024 with two crashes, were reduced to zero in 2025. The count of serious injury crashes also fell from seven to five, and their share of total crashes decreased from 5.3% to 4.0%. Consequently, the proportion of crashes resulting in no injury increased from 65.9% (87 crashes) in 2024 to 71.8% (89 crashes) in 2025.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes4%
↓-28.6%prior 7
Minor Injury17minor injury crashes13.7%
↓-10.5%prior 19
Possible Injury13possible injury crashes10.5%
↓-23.5%prior 17
No Injury89no injury crashes71.8%
↑2.3%prior 87

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

The leading contributing factors showed some shifts between periods. 'Failure to yield right of way from a stop sign' remained the top factor, increasing in count from 15 crashes in 2024 to 18 in 2025. 'Followed too close' incidents decreased from 13 to 10 but remained the second-most cited factor. Crashes attributed to 'Ran Stop Sign' were halved, dropping from 8 in 2024 to 4 in 2025, while incidents involving animals decreased slightly from 8 to 7.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign18 (14.5%)↑20.0%prior 15
Followed too close10 (8.1%)↓-23.1%prior 13
Animal7 (5.6%)↓-12.5%prior 8
Driving too fast for conditions7 (5.6%)↓-12.5%prior 8
Other (explain in narrative): Other6 (4.8%)→0.0%prior 6
Ran off road - straight6 (4.8%)↓-14.3%prior 7
Ran off road - left6 (4.8%)↓-25.0%prior 8
Improper Backing5 (4%)→0.0%prior 5
Other (explain in narrative): Vision obstructed5 (4%)
FTYROW: From yield sign5 (4%)

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 conditions under which crashes occurred varied year-over-year. While crashes in daylight remained constant at 91 incidents, crashes in dark conditions (both lighted and unlighted roadways) decreased from a combined 29 in 2024 to 19 in 2025. Regarding road surface, crashes on dry roads fell from 96 to 86, while those on snow-covered roads increased from 7 to 12. The proportion of crashes on non-dry surfaces rose slightly from 22.7% in 2024 to 24.2% in 2025.

Weather

Clear70 (58.8%)
↓-20.5%prior 88
Cloudy31 (26.1%)
↑19.2%prior 26
Blowing Snow5 (4.2%)
Severe Winds5 (4.2%)
Snow2 (1.7%)
↓-60.0%prior 5
Fog, smoke, smog2 (1.7%)
Rain2 (1.7%)
Sleet, hail1 (0.8%)
Other (explain in narrative)1 (0.8%)

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

Lighting

Daylight91 (76.5%)
→0.0%prior 91
Dark - roadway not lighted10 (8.4%)
↓-41.2%prior 17
Dark - roadway lighted9 (7.6%)
↓-25.0%prior 12
Dawn4 (3.4%)
Dusk3 (2.5%)
↓-40.0%prior 5
Dark - unknown roadway lighting2 (1.7%)

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

Road Surface

Dry86 (72.3%)
↓-10.4%prior 96
Snow12 (10.1%)
↑71.4%prior 7
Wet9 (7.6%)
→0.0%prior 9
Ice/frost7 (5.9%)
↓-36.4%prior 11
Gravel2 (1.7%)
Other (explain in narrative)1 (0.8%)
Sand1 (0.8%)
Slush1 (0.8%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes and the age of persons involved showed changes between the two periods. In 2025, Chevrolet (47 combined 'CHEV' and 'CHEVROLET' mentions) was the most common make, while Ford vehicles decreased from 47 to 33. The age distribution of persons involved shifted, with a significant decrease in the 16-20 age group from 44 individuals in 2024 to 26 in 2025. Conversely, the 65+ age group saw an increase in involvement, from 32 persons in 2024 to 38 in 2025.

Top Vehicle Makes (216 vehicles)

1
CHEV34 (15.7%)
↓-8.1%prior 37
2
FORD33 (15.3%)
↓-29.8%prior 47
3
DODG14 (6.5%)
↓-17.6%prior 17
4
CHEVROLET13 (6%)
↑30.0%prior 10
5
GMC9 (4.2%)
→0.0%prior 9
6
PETERBILT8 (3.7%)
7
JEEP8 (3.7%)
↓-38.5%prior 13
8
CHRY8 (3.7%)
↑60.0%prior 5
9
RAM8 (3.7%)
↑60.0%prior 5
10
TOYT7 (3.2%)

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

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

Sex Distribution (146 persons with recorded sex)

Male91 (62.3%)
→0.0%prior 91
Female55 (37.7%)
↓-3.5%prior 57

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: 124
  • Total persons involved: 223
  • Total vehicles involved: 216

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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