Yearly Traffic Safety Analysis

324 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Clayton County, total traffic crashes increased by 4.2%, from 311 in 2024 to 324 in 2025. While the number of injuries remained stable, the most notable year-over-year change was a significant increase in traffic fatalities, which rose from 2 to 5. Collisions involving animals remained the primary contributing factor to crashes in both periods.

324

▲ 4.2%was 311

Total Crash Events

5

▲ 150.0%was 2

Persons Killed

63

▼ -1.6%was 64

Persons Injured

5

▲ 150.0%was 2

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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, Clayton County experienced a slight upward trend in crashes, with a 4.2% increase from 311 incidents in 2024 to 324 in 2025. This increase was accompanied by a concerning rise in fatalities from 2 to 5. However, the total number of injuries reported saw a marginal decrease from 64 to 63.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

4

Motorists Killed

Prior: 2↑100.0%

2

Pedestrians Injured

Prior: 0→%

61

Motorists Injured

Prior: 63↓-3.2%

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 showed a slight shift between the two periods. The day with the most crashes changed from Friday (55 crashes) in 2024 to Wednesday (56 crashes) in 2025. The 5 p.m. hour remained the peak time for collisions in both years, though the number of crashes during this hour increased from 30 to 35.

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 worsened year-over-year. The number of fatal crashes increased from 2 to 5, raising the fatal crash rate from 0.6% to 1.5% of all incidents. Conversely, the share of crashes resulting in any level of injury (Serious, Minor, or Possible) decreased, accounting for 15.1% of all crashes in 2025 compared to 17.4% in 2024.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.5%
↑150.0%prior 2
Serious Injury5serious injury crashes1.5%
↓-16.7%prior 6
Minor Injury25minor injury crashes7.7%
↓-3.8%prior 26
Possible Injury19possible injury crashes5.9%
↓-13.6%prior 22
No Injury270no injury crashes83.3%
↑5.9%prior 255

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 were the leading contributing factor in both years, with the count increasing from 150 incidents in 2024 to 155 in 2025. 'Lost Control' remained the second-most cited factor, though its frequency decreased from 26 crashes to 22. Incidents where a driver 'Ran off road - left' increased from 14 to 17, becoming the third-most common factor in the current period.

Officer-Reported Primary Contributing Cause

Animal155 (47.8%)↑3.3%prior 150
Lost Control22 (6.8%)↓-15.4%prior 26
Ran off road - left17 (5.2%)↑21.4%prior 14
Ran off road - straight13 (4%)
FTYROW: From stop sign12 (3.7%)↑71.4%prior 7
Followed too close9 (2.8%)→0.0%prior 9
Made improper turn7 (2.2%)
Driver Distraction: Other interior distraction6 (1.9%)↓-25.0%prior 8
Driving too fast for conditions6 (1.9%)↓-40.0%prior 10
Other (explain in narrative): Other6 (1.9%)↓-57.1%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 consistent year-over-year. The majority of incidents in both periods occurred in clear weather and on dry roads. In 2025, 124 crashes happened in daylight, an increase from 115 in 2024. There was also a rise in crashes occurring on gravel road surfaces, which increased from 12 incidents in 2024 to 16 in 2025.

Weather

Clear119 (65.4%)
→0.0%prior 119
Cloudy32 (17.6%)
↑33.3%prior 24
Snow6 (3.3%)
→0.0%prior 6
Rain6 (3.3%)
↓-25.0%prior 8
Blowing Snow5 (2.7%)
Fog, smoke, smog5 (2.7%)
↓-28.6%prior 7
Severe Winds4 (2.2%)
Sleet, hail2 (1.1%)
Other (explain in narrative)2 (1.1%)
Freezing rain/drizzle1 (0.5%)

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

Lighting

Daylight124 (62.0%)
↑7.8%prior 115
Dark - roadway not lighted46 (23.0%)
↑17.9%prior 39
Dark - unknown roadway lighting17 (8.5%)
↑240.0%prior 5
Dusk5 (2.5%)
Dawn4 (2.0%)
Dark - roadway lighted4 (2.0%)
↓-55.6%prior 9

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

Road Surface

Dry123 (66.8%)
↓-0.8%prior 124
Wet19 (10.3%)
↑18.8%prior 16
Gravel16 (8.7%)
↑33.3%prior 12
Snow11 (6.0%)
→0.0%prior 11
Ice/frost11 (6.0%)
Slush2 (1.1%)
Mud, dirt1 (0.5%)
Other (explain in narrative)1 (0.5%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved in crashes in both periods. The number of Ford vehicles in crashes decreased slightly from 94 to 90, while Chevrolet involvement (combining 'CHEV' and 'CHEVROLET' entries) rose from 89 to 101. The age distribution of persons involved was similar across both years, with the 65+ age group seeing the largest increase from 70 to 76 individuals.

Top Vehicle Makes (413 vehicles)

1
FORD90 (21.8%)
↓-4.3%prior 94
2
CHEV74 (17.9%)
↑8.8%prior 68
3
CHEVROLET27 (6.5%)
↑28.6%prior 21
4
GMC17 (4.1%)
↑54.5%prior 11
5
JEEP16 (3.9%)
↓-20.0%prior 20
6
RAM15 (3.6%)
↑25.0%prior 12
7
DODG14 (3.4%)
↓-12.5%prior 16
8
TOYT13 (3.1%)
↑18.2%prior 11
9
BUIC9 (2.2%)
↓-18.2%prior 11
10
HOND8 (1.9%)
↓-27.3%prior 11

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

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

Sex Distribution (182 persons with recorded sex)

Male122 (67.0%)
↑22.0%prior 100
Female60 (33.0%)
↓-16.7%prior 72

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: 324
  • Total persons involved: 426
  • Total vehicles involved: 413

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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