Yearly Traffic Safety Analysis

48 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Audubon County recorded 48 total crashes, a 28.4% decrease from the 67 crashes documented in 2024. This downward trend was accompanied by a reduction in both fatalities, from one to zero, and total injuries, which fell from 26 to 16. A significant driver of this overall improvement was the sharp decline in crashes attributed to animals, which dropped from 19 incidents in 2024 to 7 in 2025.

48

▼ -28.4%was 67

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

16

▼ -38.5%was 26

Persons Injured

0

▼ -100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

Traffic collisions in Audubon County demonstrated a significant downward trend year-over-year. Total crashes fell by 28.4%, from 67 in 2024 to 48 in 2025. This improvement extended to crash outcomes, with total injuries decreasing by 38.5% and the single fatality from the prior year not being repeated.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1↓-100.0%

16

Motorists Injured

Prior: 25↓-36.0%

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

Year-over-year temporal crash patterns saw some shifts. In 2025, the highest crash volumes occurred on Sundays, Wednesdays, and Fridays, each with 8 incidents, whereas 2024's peak days were Tuesday and Friday with 13 crashes each. The peak hour for crashes also moved from 2 p.m. in the prior year (7 crashes) to a dual peak at 12 p.m. and 4 p.m. in the current year, which both saw 6 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

Overall crash severity improved, with fatal crashes decreasing from one in 2024 to zero in 2025. While there were no serious injury crashes in the prior period, two were recorded in 2025, representing 4.2% of all crashes. The proportion of crashes resulting in either minor or possible injuries decreased, while the share of no-injury crashes increased from 64.2% in 2024 to 72.9% in 2025.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes4.2%
Minor Injury4minor injury crashes8.3%
↓-50.0%prior 8
Possible Injury7possible injury crashes14.6%
↓-53.3%prior 15
No Injury35no injury crashes72.9%
↓-18.6%prior 43

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 significantly between periods. In 2025, 'Lost Control' became the primary factor with 11 crashes, an 83.3% increase in count from 6 incidents in 2024. Conversely, crashes involving an 'Animal,' the top factor in the prior year, saw a 63.2% decrease in count, falling from 19 to 7. 'FTYROW: From stop sign' incidents were also halved, decreasing from 4 crashes to 2.

Officer-Reported Primary Contributing Cause

Lost Control11 (22.9%)↑83.3%prior 6
Animal7 (14.6%)↓-63.2%prior 19
Failed to keep in proper lane3 (6.3%)
Driving too fast for conditions3 (6.3%)
Ran Stop Sign3 (6.3%)
Driver Distraction: Other interior distraction2 (4.2%)
Operating vehicle in an reckless, erratic, careless, negligent manner2 (4.2%)
Passing: Where prohibited by signs/markings2 (4.2%)
FTYROW: From stop sign2 (4.2%)
Driver Distraction: Passenger2 (4.2%)

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

Road & Environmental Conditions

Crashes in clear weather and daylight conditions made up a larger proportion of the total in 2025 compared to 2024, with daylight crashes increasing their share from 55.2% to 60.4% of all incidents. The percentage of crashes occurring on dry road surfaces remained stable at approximately 52% for both periods. Notably, the share of crashes in 'Dark - roadway not lighted' conditions more than doubled, rising from 7.5% of crashes in 2024 to 16.7% in 2025.

Weather

Clear31 (73.8%)
↓-8.8%prior 34
Cloudy7 (16.7%)
↓-22.2%prior 9
Snow2 (4.8%)
Blowing Snow1 (2.4%)
Rain1 (2.4%)

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

Lighting

Daylight29 (69.0%)
↓-21.6%prior 37
Dark - roadway not lighted8 (19.0%)
↑60.0%prior 5
Dark - roadway lighted5 (11.9%)

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

Road Surface

Dry25 (59.5%)
↓-28.6%prior 35
Gravel7 (16.7%)
Snow6 (14.3%)
Ice/frost2 (4.8%)
Mud, dirt1 (2.4%)
Wet1 (2.4%)
↓-83.3%prior 6

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 remained consistent, with Chevrolet and Ford vehicles ranking first and second in both years. The age distribution of people involved in crashes saw a notable shift; the 35-44 age group's involvement grew from representing 9.9% of persons in 2024 to 23.0% in 2025. In contrast, the representation of the 65+ age group decreased from 14.9% to 9.5%.

Top Vehicle Makes (69 vehicles)

1
CHEV18 (26.1%)
↓-21.7%prior 23
2
FORD15 (21.7%)
↑25.0%prior 12
3
CHEVROLET5 (7.2%)
4
DODG4 (5.8%)
↓-20.0%prior 5
5
KIA4 (5.8%)
6
DEER3 (4.3%)
7
MACK3 (4.3%)
8
PETERBILT2 (2.9%)
9
NISS2 (2.9%)
10
DODGE2 (2.9%)

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

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

Sex Distribution (43 persons with recorded sex)

Male29 (67.4%)
↓-12.1%prior 33
Female14 (32.6%)
↑7.7%prior 13

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: 48
  • Total persons involved: 74
  • Total vehicles involved: 69

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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