Yearly Traffic Safety Analysis

109 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Howard County, total crashes increased from 104 in 2024 to 109 in 2025, a rise of 4.8%. The most significant change year-over-year was the emergence of traffic fatalities, with two deaths recorded in 2025 compared to zero in the prior year. Total injuries also saw a slight increase from 21 to 23.

109

▲ 4.8%was 104

Total Crash Events

2

Persons Killed

23

▲ 9.5%was 21

Persons Injured

2

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

Overall, crash trends in Howard County showed a slight increase year-over-year. Total crashes rose by 4.8% from 104 in 2024 to 109 in 2025. Similarly, the number of people injured increased by 9.5% from 21 to 23, and two fatalities were recorded in 2025, whereas none occurred in the previous year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

22

Motorists Injured

Prior: 21↑4.8%

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 in Howard County shifted between the two periods. The peak day for crashes moved from Thursday and Friday (18 crashes each) in 2024 to Saturday (22 crashes) in 2025. The peak hour also shifted from the evening at 8 PM in the prior year (10 crashes) to the afternoon between 3 PM and 4 PM in the current year (11 crashes each hour).

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 worsened in 2025 with the introduction of fatal incidents. Two fatal crashes occurred, accounting for 1.8% of all crashes, compared to zero in 2024. While the prior year recorded 4 serious injury crashes, none were documented in the current period. The proportion of crashes resulting in no injuries increased slightly from 80.8% of all crashes in 2024 to 83.5% in 2025.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.8%
Minor Injury9minor injury crashes8.3%
↑28.6%prior 7
Possible Injury7possible injury crashes6.4%
↓-22.2%prior 9
No Injury91no injury crashes83.5%
↑8.3%prior 84

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 involving animals remained the top contributing factor in both periods, with a slight increase in count from 46 crashes in 2024 to 47 in 2025. Several other factors saw notable increases in their crash counts year-over-year. Crashes attributed to failure to yield from a stop sign more than doubled from 2 to 5, while those involving other interior distractions increased five-fold from 1 to 5. Similarly, crashes from running a stop sign increased from 1 to 4.

Officer-Reported Primary Contributing Cause

Animal47 (43.1%)↑2.2%prior 46
Other (explain in narrative): Other8 (7.3%)
Ran off road - left6 (5.5%)↑20.0%prior 5
Driver Distraction: Other interior distraction5 (4.6%)
Driving too fast for conditions5 (4.6%)→0.0%prior 5
FTYROW: From stop sign5 (4.6%)
Ran off road - straight4 (3.7%)
Ran Stop Sign4 (3.7%)
Lost Control3 (2.8%)↓-40.0%prior 5
Ran Traffic Signal2 (1.8%)

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 conditions under which crashes occurred showed some year-over-year shifts. While crashes in daylight increased from 41 to 45, incidents in dark, unlighted conditions decreased from 21 to 11. Crashes on adverse road surfaces saw a notable increase, with incidents on icy or frosty roads rising from 1 to 8 and crashes on snow-covered roads increasing from 3 to 8. Crashes attributed to blowing snow were recorded at 4 in 2025, a condition not present in the prior year's data.

Weather

Clear40 (60.6%)
↓-7.0%prior 43
Cloudy14 (21.2%)
↓-6.7%prior 15
Blowing Snow4 (6.1%)
Freezing rain/drizzle3 (4.5%)
Rain2 (3.0%)
Fog, smoke, smog1 (1.5%)
Other (explain in narrative)1 (1.5%)
Snow1 (1.5%)

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

Lighting

Daylight45 (67.2%)
↑9.8%prior 41
Dark - roadway not lighted11 (16.4%)
↓-47.6%prior 21
Dark - roadway lighted5 (7.5%)
Dusk4 (6.0%)
Dawn1 (1.5%)
Dark - unknown roadway lighting1 (1.5%)

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

Road Surface

Dry40 (60.6%)
↓-13.0%prior 46
Ice/frost8 (12.1%)
Snow8 (12.1%)
Wet7 (10.6%)
↓-36.4%prior 11
Gravel2 (3.0%)
↓-66.7%prior 6
Mud, dirt1 (1.5%)

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 in both years, with Ford-made vehicles increasing from 24 to 28 and Chevrolet-branded vehicles holding steady at 36. The number of GMC vehicles involved in crashes doubled from 5 in 2024 to 10 in 2025. There was a notable shift in the age of persons involved in crashes, with the 16-20 age group increasing from 15 to 22 individuals and the 55-64 age group increasing from 12 to 21 individuals.

Top Vehicle Makes (151 vehicles)

1
FORD28 (18.5%)
↑16.7%prior 24
2
CHEV22 (14.6%)
↑4.8%prior 21
3
CHEVROLET14 (9.3%)
↓-17.6%prior 17
4
GMC10 (6.6%)
↑100.0%prior 5
5
JEEP8 (5.3%)
↑33.3%prior 6
6
TOYOTA7 (4.6%)
↑40.0%prior 5
7
HOND5 (3.3%)
8
BUIC5 (3.3%)
9
NISSAN4 (2.6%)
10
CHRY4 (2.6%)

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

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

Sex Distribution (76 persons with recorded sex)

Male45 (59.2%)
↓-11.8%prior 51
Female31 (40.8%)
↑72.2%prior 18

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 10, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 109
  • Total persons involved: 154
  • Total vehicles involved: 151

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 10, 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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