Yearly Traffic Safety Analysis

255 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Floyd County recorded 255 total crashes, a 1.2% increase from the 252 crashes documented in 2024. While the overall crash volume remained relatively stable, the outcomes shifted, with total injuries decreasing by 14.1% from 64 to 55, while fatalities doubled from one to two. The most significant year-over-year change was a 20% increase in the count of crashes involving animals, which rose from 105 to 126 incidents.

255

▲ 1.2%was 252

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

55

▼ -14.1%was 64

Persons Injured

2

▲ 100.0%was 1

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 Floyd County shows a slight year-over-year increase of 1.2%, from 252 to 255 incidents. However, this near-stable total masks a change in outcomes; total injuries declined from 64 to 55, while the number of fatalities rose from one to two. This indicates a minor increase in crash frequency but a mixed severity trend.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

0

Other Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 1→0.0%

51

Motorists Injured

Prior: 63↓-19.0%

3

Other Injured

Prior: 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

Temporal crash patterns saw a notable shift between the two periods. While Friday remained the peak day for crashes with an identical count of 49 in both 2025 and 2024, the peak hour for incidents moved significantly. In 2024, the peak was the 5 p.m. hour with 30 crashes, whereas in 2025, the peak shifted to the 9 p.m. hour, which saw 20 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 of crashes shifted year-over-year, even as the total number of incidents remained similar. The number of fatal crashes doubled from one in 2024 to two in 2025, increasing their share of all crashes from 0.4% to 0.8%. Conversely, serious injury crashes saw a significant reduction, falling from 8 incidents (3.2% of total) in the prior year to just 2 incidents (0.8% of total) in the current year. The proportion of non-injury crashes increased from 81.0% to 84.3%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.8%
↑100.0%prior 1
Serious Injury2serious injury crashes0.8%
↓-75.0%prior 8
Minor Injury18minor injury crashes7.1%
↓-14.3%prior 21
Possible Injury18possible injury crashes7.1%
→0.0%prior 18
No Injury215no injury crashes84.3%
↑5.4%prior 204

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, with its count increasing by 20% from 105 crashes in 2024 to 126 in 2025. "Ran off road - left" was the second-leading cause in both years, with its count remaining stable at 16 and 17, respectively. Notably, crashes attributed to "Failure to yield right-of-way from a stop sign" decreased from 14 incidents in the prior year to 8 in the current period.

Officer-Reported Primary Contributing Cause

Animal126 (49.4%)↑20.0%prior 105
Ran off road - left17 (6.7%)↑6.3%prior 16
Driver Distraction: Other interior distraction11 (4.3%)↑22.2%prior 9
Lost Control9 (3.5%)↓-18.2%prior 11
FTYROW: From stop sign8 (3.1%)↓-42.9%prior 14
FTYROW: At uncontrolled intersection7 (2.7%)↓-12.5%prior 8
Ran Stop Sign7 (2.7%)→0.0%prior 7
Driving too fast for conditions7 (2.7%)↓-36.4%prior 11
Driver Distraction: Inattentive/lost in thought5 (2%)
Ran off road - straight5 (2%)↓-44.4%prior 9

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

Road & Environmental Conditions

Year-over-year comparison shows a shift in crash conditions. While crashes on dry roads decreased from 100 to 89, incidents during snowy weather increased; crashes in snow or blowing snow conditions rose from 9 in 2024 to 14 in 2025. The total number of crashes on adverse road surfaces like snow, ice, or slush remained nearly identical at 26 in 2025 versus 27 in 2024. Crashes in daylight were stable, with 103 in the current period compared to 104 in the prior.

Weather

Clear75 (56.8%)
↓-18.5%prior 92
Cloudy32 (24.2%)
↓-3.0%prior 33
Blowing Snow7 (5.3%)
Snow7 (5.3%)
↑40.0%prior 5
Rain5 (3.8%)
↓-54.5%prior 11
Fog, smoke, smog2 (1.5%)
Severe Winds2 (1.5%)
Freezing rain/drizzle2 (1.5%)

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

Lighting

Daylight103 (73.6%)
↓-1.0%prior 104
Dark - roadway not lighted17 (12.1%)
↓-22.7%prior 22
Dark - roadway lighted9 (6.4%)
↓-35.7%prior 14
Dark - unknown roadway lighting6 (4.3%)
Dusk4 (2.9%)
Dawn1 (0.7%)
↓-83.3%prior 6

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

Road Surface

Dry89 (67.4%)
↓-11.0%prior 100
Wet13 (9.8%)
↓-7.1%prior 14
Snow12 (9.1%)
↑9.1%prior 11
Ice/frost11 (8.3%)
→0.0%prior 11
Gravel3 (2.3%)
↓-72.7%prior 11
Slush3 (2.3%)
↓-40.0%prior 5
Mud, dirt1 (0.8%)

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

Vehicles & Demographics

The vehicle makes most frequently involved in crashes shifted year-over-year. In 2025, Chevrolet was the most common make with 79 vehicles, overtaking Ford, which had 50. This reverses the prior year's ranking, where Ford led with 63 vehicles to Chevrolet's 59. There was also a change in the age distribution of persons involved in crashes; the 55-64 age group saw a notable increase from 41 to 57 persons, while the 26-34 age group decreased from 66 to 52 persons.

Top Vehicle Makes (352 vehicles)

1
CHEV79 (22.4%)
↑33.9%prior 59
2
FORD50 (14.2%)
↓-20.6%prior 63
3
CHRY18 (5.1%)
↑100.0%prior 9
4
DODG16 (4.5%)
↓-15.8%prior 19
5
GMC15 (4.3%)
↓-11.8%prior 17
6
CHEVROLET14 (4%)
↑75.0%prior 8
7
BUIC13 (3.7%)
↑62.5%prior 8
8
TOYT13 (3.7%)
↑18.2%prior 11
9
NISS11 (3.1%)
→0.0%prior 11
10
DODGE11 (3.1%)
↑57.1%prior 7

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

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

Sex Distribution (152 persons with recorded sex)

Male94 (61.8%)
↓-7.8%prior 102
Female58 (38.2%)
↑3.6%prior 56

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: 255
  • Total persons involved: 368
  • Total vehicles involved: 352

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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