Yearly Traffic Safety Analysis

86 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Worth County, total crashes decreased by 12.2% from 98 in 2024 to 86 in 2025. This overall reduction in collisions was accompanied by a significant drop in traffic fatalities, which fell from 4 in the prior year to 1 in the current year. The most notable shift was this 75% decrease in the number of people killed in crashes.

86

▼ -12.2%was 98

Total Crash Events

1

▼ -75.0%was 4

Persons Killed

25

▼ -26.5%was 34

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 Worth County indicates a downward trend year-over-year. Total crashes fell from 98 to 86, a 12.2% decrease. This positive trend extended to crash outcomes, with total injuries declining by 26.5% from 34 to 25, and total fatalities decreasing from 4 to 1.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 4↓-75.0%

1

Pedestrians Injured

Prior: 0→%

24

Motorists Injured

Prior: 34↓-29.4%

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 shifted year-over-year. The most frequent day for crashes moved from Sunday in the prior period, which saw 24 incidents, to Friday in the current period with 15 incidents. The peak hour for collisions also changed, shifting from 5 p.m. in 2024 (9 crashes) to 9 p.m. in 2025 (7 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 overall severity of crashes lessened compared to the previous year. The fatal crash rate declined from 2.04 to 1.16 per 100 crashes. While the share of fatal crashes dropped from 2.0% to 1.2%, the proportion of serious injury crashes increased from 1.0% to 3.5%. Crashes resulting in no injuries represented a larger share of the total, increasing from 71.4% to 74.4%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.2%
↓-50.0%prior 2
Serious Injury3serious injury crashes3.5%
↑200.0%prior 1
Minor Injury12minor injury crashes14%
↓-7.7%prior 13
Possible Injury6possible injury crashes7%
↓-50.0%prior 12
No Injury64no injury crashes74.4%
↓-8.6%prior 70

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, accounting for 32 crashes each year. "Lost Control" was also a consistent factor, cited in 8 crashes in both 2024 and 2025. The number of crashes attributed to "Ran off road - left" saw a notable decrease, falling from 7 incidents in the prior year to 3 in the current year.

Officer-Reported Primary Contributing Cause

Animal32 (37.2%)→0.0%prior 32
Ran off road - straight8 (9.3%)↑14.3%prior 7
Lost Control8 (9.3%)→0.0%prior 8
Driving too fast for conditions5 (5.8%)↓-16.7%prior 6
FTYROW: From stop sign4 (4.7%)
Made improper turn3 (3.5%)
Ran off road - left3 (3.5%)↓-57.1%prior 7
Ran off road - right2 (2.3%)
FTYROW: Making left turn1 (1.2%)
Improper Backing1 (1.2%)

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

Road & Environmental Conditions

The proportion of crashes occurring on dry roads decreased from 50.0% in the prior period to 36.0% in the current period. Conversely, the share of crashes on adverse road surfaces increased; incidents on wet roads grew from 4.1% to 8.1% of all crashes, and those on icy or frosty roads rose from 7.1% to 9.3%. The percentage of crashes in daylight conditions remained relatively stable, moving from 46.9% to 44.2%.

Weather

Clear28 (51.9%)
↓-37.8%prior 45
Cloudy8 (14.8%)
↑14.3%prior 7
Snow5 (9.3%)
Rain5 (9.3%)
Blowing Snow4 (7.4%)
Severe Winds3 (5.6%)
Blowing sand, soil, dirt1 (1.9%)

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

Lighting

Daylight38 (70.4%)
↓-17.4%prior 46
Dark - roadway not lighted13 (24.1%)
↓-31.6%prior 19
Dark - roadway lighted2 (3.7%)
Dusk1 (1.9%)

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

Road Surface

Dry31 (57.4%)
↓-36.7%prior 49
Ice/frost8 (14.8%)
↑14.3%prior 7
Wet7 (13.0%)
Snow6 (11.1%)
Gravel1 (1.9%)
Slush1 (1.9%)

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

Vehicles & Demographics

There was a shift in the top vehicle makes involved in crashes year-over-year. Ford became the most frequent make with 22 vehicles, up from 16, while Chevrolet-branded vehicles (CHEV/CHEVROLET) decreased from 28 to 11. The age demographics of persons involved also changed, with the 65+ age group increasing from 20 to 25 individuals to become the largest cohort. The 35-44 age group, previously the largest with 26 people, decreased to 21.

Top Vehicle Makes (117 vehicles)

1
FORD22 (18.8%)
↑37.5%prior 16
2
CHEV7 (6%)
↓-63.2%prior 19
3
FREIGHTLINER7 (6%)
↑40.0%prior 5
4
CHRY5 (4.3%)
5
RAM5 (4.3%)
6
TOYO5 (4.3%)
→0.0%prior 5
7
CHEVROLET4 (3.4%)
↓-55.6%prior 9
8
NISSAN4 (3.4%)
9
HONDA4 (3.4%)
10
JEEP4 (3.4%)
↓-55.6%prior 9

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

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

Sex Distribution (61 persons with recorded sex)

Male43 (70.5%)
→0.0%prior 43
Female18 (29.5%)
↓-10.0%prior 20

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: 86
  • Total persons involved: 122
  • Total vehicles involved: 117

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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