Yearly Traffic Safety Analysis

191 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Mills County recorded 191 total crashes, a 15.5% decrease from the 226 crashes reported in 2024. While total fatalities remained constant at 3, the number of injuries fell by 24.1% from 83 to 63. The overall reduction in crash volume and associated injuries was the most notable year-over-year shift.

191

▼ -15.5%was 226

Total Crash Events

3

Persons Killed

63

▼ -24.1%was 83

Persons Injured

2

▼ -33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (3) 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

Crash data for Mills County indicates a downward trend year-over-year. Total crashes decreased by 15.5%, from 226 in 2024 to 191 in 2025. This decline was also reflected in total injuries, which dropped by 24.1% from 83 to 63, while fatalities held steady at 3 for both periods.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 3→0.0%

63

Motorists Injured

Prior: 83↓-24.1%

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 some changes between 2024 and 2025. While Friday remained the peak day for crashes in both years, the volume on Fridays decreased significantly from 65 to 32 incidents. The peak hour for crashes also remained consistent at 5 p.m., with a reduction from 22 crashes in the prior year to 18 in the current year. The daily distribution of crashes became more even in 2025, unlike the previous year where Friday was a significant outlier.

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 between the two periods. The number of fatal crashes decreased from 3 in 2024 to 2 in 2025, and the proportion of crashes resulting in any injury fell from 29.6% to 25.7%. While there was an increase in 'Serious Injury' crashes from 6 to 9, 'Minor Injury' crashes saw a significant drop from 39 to 20. Crashes with 'No Injury' constituted a larger share of the total in 2025, rising from 70.4% to 74.3%.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1%
↓-33.3%prior 3
Serious Injury9serious injury crashes4.7%
↑50.0%prior 6
Minor Injury20minor injury crashes10.5%
↓-48.7%prior 39
Possible Injury18possible injury crashes9.4%
↓-5.3%prior 19
No Injury142no injury crashes74.3%
↓-10.7%prior 159

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 for crashes shifted year-over-year. In 2025, 'Lost Control' was the most cited factor with 27 incidents, an increase from 16 incidents in the prior year. 'Animal' involvement, which was the top factor in 2024 with 33 crashes, decreased to 26 crashes in 2025, becoming the second-leading cause. Notably, crashes attributed to 'Driving too fast for conditions' were more than halved, dropping from 17 incidents in 2024 to 8 in 2025.

Officer-Reported Primary Contributing Cause

Lost Control27 (14.1%)↑68.8%prior 16
Animal26 (13.6%)↓-21.2%prior 33
Ran off road - straight19 (9.9%)↓-5.0%prior 20
FTYROW: From stop sign13 (6.8%)↑30.0%prior 10
Followed too close10 (5.2%)
Driving too fast for conditions8 (4.2%)↓-52.9%prior 17
Driver Distraction: Other interior distraction7 (3.7%)→0.0%prior 7
Other (explain in narrative): Other7 (3.7%)↓-58.8%prior 17
Ran off road - left6 (3.1%)↓-60.0%prior 15
Improper Backing5 (2.6%)

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

Road & Environmental Conditions

Crashes were more likely to occur in clear weather and on dry roads in 2025 compared to 2024. The proportion of crashes on dry road surfaces increased from 64.6% in 2024 to 74.9% in 2025. Correspondingly, crashes on adverse surfaces like ice, snow, or wet roads saw a notable decrease in count from 56 to 31. Similarly, while clear weather remained the dominant condition in both years, its share of total crashes grew from 69.5% to 74.9%.

Weather

Clear143 (82.2%)
↓-8.9%prior 157
Cloudy16 (9.2%)
↑60.0%prior 10
Rain6 (3.4%)
↓-14.3%prior 7
Snow3 (1.7%)
↓-40.0%prior 5
Blowing Snow2 (1.1%)
Other (explain in narrative)1 (0.6%)
Freezing rain/drizzle1 (0.6%)
↓-91.7%prior 12
Severe Winds1 (0.6%)
Fog, smoke, smog1 (0.6%)

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

Lighting

Daylight108 (61.0%)
↓-17.6%prior 131
Dark - roadway not lighted36 (20.3%)
↓-12.2%prior 41
Dark - roadway lighted15 (8.5%)
↑15.4%prior 13
Dusk8 (4.5%)
↓-20.0%prior 10
Dawn5 (2.8%)
Dark - unknown roadway lighting5 (2.8%)
↓-37.5%prior 8

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

Road Surface

Dry143 (82.2%)
↓-2.1%prior 146
Wet12 (6.9%)
↓-20.0%prior 15
Gravel8 (4.6%)
↑33.3%prior 6
Ice/frost7 (4.0%)
↓-66.7%prior 21
Snow3 (1.7%)
↓-75.0%prior 12
Other (explain in narrative)1 (0.6%)

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

Vehicles & Demographics

Vehicle and person demographics showed notable shifts year-over-year. While Ford was the most common vehicle make involved in crashes in 2024 with 69 vehicles, its count dropped to 49 in 2025. In contrast, Chevrolet-branded vehicles increased from 45 to 57, becoming the most frequent make. The age distribution of persons involved in crashes also changed; the 26-34 age group saw its involvement increase from 44 to 57 individuals, while involvement for the 35-44 and 45-54 age groups decreased from 59 to 35 and 50 to 32, respectively.

Top Vehicle Makes (294 vehicles)

1
FORD49 (16.7%)
↓-29.0%prior 69
2
CHEV38 (12.9%)
↑31.0%prior 29
3
CHEVROLET19 (6.5%)
↑18.8%prior 16
4
JEEP16 (5.4%)
↓-27.3%prior 22
5
NISSAN13 (4.4%)
↑160.0%prior 5
6
NISS11 (3.7%)
↑120.0%prior 5
7
HONDA10 (3.4%)
↓-9.1%prior 11
8
GMC9 (3.1%)
↓-25.0%prior 12
9
KIA9 (3.1%)
↓-18.2%prior 11
10
DODG9 (3.1%)
↓-10.0%prior 10

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

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

Sex Distribution (195 persons with recorded sex)

Male111 (56.9%)
↓-5.1%prior 117
Female84 (43.1%)
↓-8.7%prior 92

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: 191
  • Total persons involved: 309
  • Total vehicles involved: 294

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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