Yearly Traffic Safety Analysis

144 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Greene County, total vehicle crashes decreased by 6.5% from 154 in 2024 to 144 in 2025. This overall decline was accompanied by a significant year-over-year reduction in crash severity. The most notable shift was a 50% drop in fatalities, from 4 in the prior period to 2 in the current period, and a 25.8% decrease in total injuries, from 66 to 49.

144

▼ -6.5%was 154

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

49

▼ -25.8%was 66

Persons Injured

2

▼ -50.0%was 4

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

Crash data for Greene County indicates a positive downward trend year-over-year. The total number of crashes fell from 154 to 144. This trend extends to crash outcomes, with total fatalities decreasing from 4 to 2 and total injuries declining from 66 to 49.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 4↓-50.0%

0

Other Killed

Prior: 0→0.0%

48

Motorists Injured

Prior: 65↓-26.2%

1

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

The timing of crashes shifted between the two periods. In 2025, Wednesday became the peak day for crashes with 31 incidents, a notable increase from 20 crashes on Wednesdays in the prior year, when Friday was the peak day with 28 incidents. The peak hour also moved from the afternoon to the morning, shifting from 4 p.m. (13 crashes) in 2024 to 6 a.m. (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 in 2025 compared to the previous year. The number of fatal crashes was halved, falling from 4 to 2, and the count of serious injury crashes also dropped from 6 to 3. Consequently, the fatal crash rate declined from 2.6% to 1.4% of all crashes. The proportion of crashes resulting in no injuries remained stable, accounting for 69.4% of incidents in 2025 versus 68.8% in 2024.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.4%
↓-50.0%prior 4
Serious Injury3serious injury crashes2.1%
↓-50.0%prior 6
Minor Injury19minor injury crashes13.2%
→0.0%prior 19
Possible Injury20possible injury crashes13.9%
↑5.3%prior 19
No Injury100no injury crashes69.4%
↓-5.7%prior 106

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 leading contributing factor in both periods, though the count of these incidents decreased by 12% from 50 in 2024 to 44 in 2025. A significant change was observed in crashes attributed to running a stop sign, which fell from 11 incidents to 4, a 63.6% decrease in count. Conversely, crashes involving a vehicle running off a straight road increased from 3 to 8 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal44 (30.6%)↓-12.0%prior 50
Other (explain in narrative): Other16 (11.1%)↑45.5%prior 11
Ran off road - straight8 (5.6%)
Ran off road - left8 (5.6%)↑14.3%prior 7
Lost Control8 (5.6%)↓-20.0%prior 10
Driving too fast for conditions7 (4.9%)→0.0%prior 7
FTYROW: From stop sign4 (2.8%)↓-42.9%prior 7
Ran Stop Sign4 (2.8%)↓-63.6%prior 11
FTYROW: From parked position3 (2.1%)
Ran off road - right3 (2.1%)

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

Road & Environmental Conditions

While clear weather and daylight conditions were predominant in both years, there was a shift in road surface conditions associated with crashes. In 2025, the number of crashes on dry roads decreased from 87 to 70. Correspondingly, incidents on roads affected by snow, ice, or slush increased from 14 in 2024 to 20 in 2025. Crashes in snowy weather conditions increased from 4 to 7 incidents.

Weather

Clear85 (78.0%)
↓-7.6%prior 92
Cloudy9 (8.3%)
↓-10.0%prior 10
Snow7 (6.4%)
Blowing Snow3 (2.8%)
Rain2 (1.8%)
↓-66.7%prior 6
Severe Winds1 (0.9%)
Fog, smoke, smog1 (0.9%)
Freezing rain/drizzle1 (0.9%)

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

Lighting

Daylight78 (67.8%)
→0.0%prior 78
Dark - roadway not lighted22 (19.1%)
↓-15.4%prior 26
Dark - roadway lighted5 (4.3%)
Dark - unknown roadway lighting5 (4.3%)
Dusk4 (3.5%)
Dawn1 (0.9%)

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

Road Surface

Dry70 (64.2%)
↓-19.5%prior 87
Snow12 (11.0%)
↑140.0%prior 5
Gravel10 (9.2%)
↑100.0%prior 5
Wet8 (7.3%)
↓-20.0%prior 10
Ice/frost7 (6.4%)
↑16.7%prior 6
Slush1 (0.9%)
Mud, dirt1 (0.9%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes shifted year-over-year, with Ford becoming the most frequent make (40 vehicles) in 2025, up from 37 in the prior year. Chevrolet, the top make in 2024 with 42 vehicles, saw its involvement decrease to 34 vehicles. The age demographics of persons involved in crashes remained largely consistent, with the 16-20 and 26-34 age groups each accounting for 37 individuals in the current period, similar to the prior year's figures.

Top Vehicle Makes (204 vehicles)

1
FORD40 (19.6%)
↑8.1%prior 37
2
CHEV34 (16.7%)
↓-19.0%prior 42
3
DODG10 (4.9%)
↑42.9%prior 7
4
GMC9 (4.4%)
↑50.0%prior 6
5
TOYO9 (4.4%)
↑12.5%prior 8
6
HOND9 (4.4%)
↑80.0%prior 5
7
NR6 (2.9%)
8
BUIC6 (2.9%)
↑20.0%prior 5
9
NISS6 (2.9%)
↓-45.5%prior 11
10
RAM6 (2.9%)

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

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

Sex Distribution (110 persons with recorded sex)

Male56 (50.9%)
↓-13.8%prior 65
Female54 (49.1%)
↑28.6%prior 42

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: 144
  • Total persons involved: 211
  • Total vehicles involved: 204

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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