Yearly Traffic Safety Analysis

329 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Winneshiek County, total traffic crashes decreased by 6% from 350 in 2024 to 329 in 2025. Despite the overall decline in collisions, the number of fatalities rose from 2 to 3 in the current period. The most significant contributing factor in both years was collisions with animals, although these incidents saw a notable decrease from 160 in the prior year to 130 in the current year.

329

▼ -6.0%was 350

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

97

Persons Injured

3

▲ 50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 crashes in Winneshiek County showed a downward trend, falling by 6% from 350 incidents in 2024 to 329 in 2025. While the total number of injuries remained unchanged at 97 for both years, the number of fatalities increased from 2 to 3. The number of crashes involving a suspected DUI driver also decreased from 17 to 10.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 2↑50.0%

2

Cyclists Injured

Prior: 1↑100.0%

95

Motorists Injured

Prior: 96↓-1.0%

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 timing of crashes shifted slightly year-over-year. In 2025, the peak day for crashes was Tuesday with 60 incidents, a change from the prior year when Friday was the peak day with 61 crashes. However, the 5 p.m. hour remained the most frequent time for crashes in both periods, accounting for 34 crashes in 2025 and 31 in 2024.

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 of crashes shifted year-over-year, with a rise in fatal incidents. Fatal crashes increased from 2 in 2024 to 3 in 2025, representing 0.9% of all crashes compared to 0.6% previously. Conversely, crashes resulting in serious injuries decreased from 11 to 7, and minor injury crashes fell from 42 to 31. Crashes involving possible injuries saw an increase from 27 to 34.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.9%
↑50.0%prior 2
Serious Injury7serious injury crashes2.1%
↓-36.4%prior 11
Minor Injury31minor injury crashes9.4%
↓-26.2%prior 42
Possible Injury34possible injury crashes10.3%
↑25.9%prior 27
No Injury254no injury crashes77.2%
↓-5.2%prior 268

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 involving an animal remained the top contributing factor in both periods, though the count of such incidents decreased by 18.8% from 160 in 2024 to 130 in 2025. Losing control of the vehicle became the second-leading factor in 2025 with 27 crashes, up from 24 crashes in the prior year. Crashes attributed to following too closely saw a notable drop in count, from 17 incidents in 2024 to 10 in 2025.

Officer-Reported Primary Contributing Cause

Animal130 (39.5%)↓-18.8%prior 160
Lost Control27 (8.2%)↑12.5%prior 24
Driving too fast for conditions23 (7%)↓-8.0%prior 25
FTYROW: From stop sign15 (4.6%)↑114.3%prior 7
Other (explain in narrative): Other14 (4.3%)↑55.6%prior 9
Ran off road - straight11 (3.3%)↓-8.3%prior 12
Driver Distraction: Other interior distraction11 (3.3%)
Followed too close10 (3%)↓-41.2%prior 17
FTYROW: Making left turn9 (2.7%)↑28.6%prior 7
Ran off road - left8 (2.4%)↓-42.9%prior 14

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with the majority of incidents in both periods occurring in daylight, on dry roads, and in clear weather. Crashes in daylight increased from 123 to 142, while those on snow-covered road surfaces rose from 12 to 21. The number of crashes on dry roads was identical in both years at 153, and incidents during clear weather were also stable, with 136 in 2025 compared to 134 in 2024.

Weather

Clear136 (63.8%)
↑1.5%prior 134
Cloudy48 (22.5%)
↑2.1%prior 47
Snow9 (4.2%)
↑50.0%prior 6
Rain5 (2.3%)
↓-16.7%prior 6
Blowing Snow4 (1.9%)
Fog, smoke, smog4 (1.9%)
↓-33.3%prior 6
Freezing rain/drizzle4 (1.9%)
Other (explain in narrative)2 (0.9%)
Severe Winds1 (0.5%)

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

Lighting

Daylight142 (63.7%)
↑15.4%prior 123
Dark - roadway not lighted46 (20.6%)
↓-8.0%prior 50
Dark - roadway lighted13 (5.8%)
↓-27.8%prior 18
Dusk11 (4.9%)
↑37.5%prior 8
Dark - unknown roadway lighting6 (2.7%)
↓-14.3%prior 7
Dawn5 (2.2%)
↓-44.4%prior 9

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

Road Surface

Dry153 (71.8%)
→0.0%prior 153
Snow21 (9.9%)
↑75.0%prior 12
Wet13 (6.1%)
↓-13.3%prior 15
Ice/frost10 (4.7%)
→0.0%prior 10
Gravel7 (3.3%)
↓-41.7%prior 12
Slush5 (2.3%)
→0.0%prior 5
Other (explain in narrative)3 (1.4%)
Mud, dirt1 (0.5%)

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 were consistent, with Ford and Chevrolet models being the most common in both 2025 and 2024. The age demographics of persons involved in crashes showed a notable shift; the number of individuals aged 65 and older increased from 61 to 85. In contrast, the number of people in the 16-20 age group involved in crashes decreased from 73 to 60.

Top Vehicle Makes (448 vehicles)

1
FORD89 (19.9%)
↓-11.9%prior 101
2
CHEV69 (15.4%)
↓-13.8%prior 80
3
CHEVROLET29 (6.5%)
→0.0%prior 29
4
GMC26 (5.8%)
↑8.3%prior 24
5
JEEP26 (5.8%)
↓-3.7%prior 27
6
TOYT14 (3.1%)
↑40.0%prior 10
7
DODG13 (2.9%)
↑8.3%prior 12
8
BUIC12 (2.7%)
→0.0%prior 12
9
TOYOTA11 (2.5%)
↑10.0%prior 10
10
KIA11 (2.5%)
↑120.0%prior 5

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

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

Sex Distribution (200 persons with recorded sex)

Male118 (59.0%)
↑20.4%prior 98
Female82 (41.0%)
↓-8.9%prior 90

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: 329
  • Total persons involved: 472
  • Total vehicles involved: 448

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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