Yearly Traffic Safety Analysis

60 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Van Buren County, total traffic crashes decreased from 71 in the prior year to 60 in the current year, a 15.5% reduction. While the number of injuries remained unchanged at 33, the most significant year-over-year shift was the elimination of traffic fatalities, which dropped from 2 to 0.

60

▼ -15.5%was 71

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

33

Persons Injured

0

▼ -100.0%was 2

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

Overall, Van Buren County saw a downward trend in traffic incidents, with total crashes falling by 15.5% from 71 to 60. This positive trend included a drop in fatal crashes from 2 to 0, though the total number of people injured in crashes held steady at 33 for both periods.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 2↓-100.0%

0

Other Killed

Prior: 0→0.0%

2

Cyclists Injured

Prior: 1↑100.0%

28

Motorists Injured

Prior: 32↓-12.5%

3

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 the current year, the peak days for crashes were Wednesday and Thursday, each with 12 incidents, and the peak hour was 3 p.m. with 8 crashes. This contrasts with the prior year, when Tuesday was the peak day (13 crashes) and the most frequent crash time was 11 p.m. (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

Crash severity improved year-over-year, with fatal crashes decreasing from 2 (2.8% of total) to 0. The number of serious injury crashes also saw a slight reduction from 7 to 6. However, the count of minor injury crashes increased from 10 to 14, representing a larger share of total incidents (23.3% vs. 14.1% previously).

Outcome by Severity (Crash Events)

Serious Injury6serious injury crashes10%
↓-14.3%prior 7
Minor Injury14minor injury crashes23.3%
↑40.0%prior 10
Possible Injury6possible injury crashes10%
↓-14.3%prior 7
No Injury34no injury crashes56.7%
↓-24.4%prior 45

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 primary contributing factors for crashes changed year-over-year. In the current period, "Lost Control" was the leading cause with 12 incidents, an increase from 9 in the prior period. Crashes involving an animal, which was the top factor in the prior year with 13 incidents, decreased significantly to 5 incidents. Incidents where a driver "Followed too close" tripled, increasing from 2 to 6.

Officer-Reported Primary Contributing Cause

Lost Control12 (20%)↑33.3%prior 9
Followed too close6 (10%)
Animal5 (8.3%)↓-61.5%prior 13
Ran off road - straight4 (6.7%)↓-50.0%prior 8
Driver Distraction: Inattentive/lost in thought4 (6.7%)
Swerving/Evasive Action3 (5%)
Passing: Other passing (explain in narrative)3 (5%)
FTYROW: From stop sign3 (5%)
Made improper turn2 (3.3%)
Driving too fast for conditions2 (3.3%)

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 the current period were more concentrated in favorable conditions compared to the prior year. The proportion of crashes occurring in daylight increased from 59.2% to 75.0%, while incidents on dry roads rose from 64.8% to 78.3%. Correspondingly, crashes in adverse conditions decreased, with incidents in the dark on unlit roads falling from 18 to 7, and crashes on wet roads dropping from 8 to 4.

Weather

Clear45 (80.4%)
↑7.1%prior 42
Cloudy7 (12.5%)
↓-30.0%prior 10
Rain2 (3.6%)
↓-66.7%prior 6
Freezing rain/drizzle1 (1.8%)
Snow1 (1.8%)

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

Lighting

Daylight45 (81.8%)
↑7.1%prior 42
Dark - roadway not lighted7 (12.7%)
↓-61.1%prior 18
Dark - roadway lighted2 (3.6%)
Dusk1 (1.8%)

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

Road Surface

Dry47 (83.9%)
↑2.2%prior 46
Wet4 (7.1%)
↓-50.0%prior 8
Snow2 (3.6%)
Mud, dirt1 (1.8%)
Gravel1 (1.8%)
Ice/frost1 (1.8%)

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

Vehicles & Demographics

Ford and Chevrolet remained the top two vehicle makes involved in crashes, though the number of Fords involved decreased from 28 to 18. A notable shift occurred in the age distribution of persons involved in crashes. While involvement for the 16-20 age group decreased from 22 to 18 individuals, there was a marked increase in older age groups, with the 55-64 group's involvement rising from 10 to 18 people and the 65+ group increasing from 16 to 20 people.

Top Vehicle Makes (85 vehicles)

1
FORD18 (21.2%)
↓-35.7%prior 28
2
CHEV12 (14.1%)
↑9.1%prior 11
3
GMC8 (9.4%)
4
DODG7 (8.2%)
↑16.7%prior 6
5
PETERBILT4 (4.7%)
6
NR3 (3.5%)
7
CHEVROLET3 (3.5%)
↓-62.5%prior 8
8
DODGE3 (3.5%)
9
PONT2 (2.4%)
10
MACK2 (2.4%)

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

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

Sex Distribution (51 persons with recorded sex)

Male38 (74.5%)
↓-9.5%prior 42
Female13 (25.5%)
↓-7.1%prior 14

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: 60
  • Total persons involved: 94
  • Total vehicles involved: 85

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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