Yearly Traffic Safety Analysis

154 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Monona County, total vehicle crashes decreased by 9.9% from 171 in 2024 to 154 in 2025. Despite the overall reduction in crashes and a 31.3% drop in injuries from 64 to 44, the number of fatalities tripled, increasing from one person in the prior period to three in the current period.

154

▼ -9.9%was 171

Total Crash Events

3

▲ 200.0%was 1

Persons Killed

44

▼ -31.3%was 64

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

The overall trend shows a decrease in the total volume of crashes and injuries year-over-year. Total crashes fell by 17 incidents from 171 to 154, and total injuries decreased from 64 to 44. However, this downward trend in crash frequency was accompanied by a rise in crash severity, with fatalities increasing from one to three.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 1↑200.0%

2

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

41

Motorists Injured

Prior: 64↓-35.9%

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, the afternoon peak hour for crashes remained consistent at 3 p.m., which recorded 15 crashes in both 2024 and 2025. However, a new morning peak emerged at 10 a.m. in 2025 with 15 crashes, more than double the 7 crashes seen in the same hour in the prior year. The most common day for crashes shifted from Saturday (33 crashes) in the prior period to a tie between Thursday and Saturday (25 crashes each) in the current period.

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 increased in 2025 compared to 2024, despite fewer overall incidents. The number of fatal crashes rose from one to three, and the fatal crash rate increased from 0.58% to 1.95% of all crashes. Conversely, crashes resulting in serious injuries saw a significant decrease, falling from 12 incidents in 2024 to 5 in 2025.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.9%
↑200.0%prior 1
Serious Injury5serious injury crashes3.2%
↓-58.3%prior 12
Minor Injury22minor injury crashes14.3%
↓-4.3%prior 23
Possible Injury16possible injury crashes10.4%
↓-30.4%prior 23
No Injury108no injury crashes70.1%
↓-3.6%prior 112

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 an animal remained the top contributing factor in both periods, though the count decreased from 35 crashes in 2024 to 29 in 2025. The number of crashes attributed to 'Lost Control' increased by 50%, from 12 to 18 incidents, making it the second-leading factor in the current period. Similarly, crashes involving 'Driving too fast for conditions' rose from 13 to 18 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal29 (18.8%)↓-17.1%prior 35
Lost Control18 (11.7%)↑50.0%prior 12
Driving too fast for conditions18 (11.7%)↑38.5%prior 13
Driver Distraction: Other interior distraction11 (7.1%)→0.0%prior 11
Ran off road - left9 (5.8%)↓-18.2%prior 11
Other (explain in narrative): Other8 (5.2%)↓-20.0%prior 10
FTYROW: From stop sign7 (4.5%)→0.0%prior 7
Followed too close6 (3.9%)↓-25.0%prior 8
Ran Stop Sign5 (3.2%)
Ran off road - straight4 (2.6%)↓-55.6%prior 9

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

Road & Environmental Conditions

There was a notable shift in crash conditions year-over-year. Crashes on snowy road surfaces increased from 13 in 2024 to 23 in 2025, and incidents during blowing snow conditions rose from 2 to 9. Concurrently, crashes on dry roads decreased from 111 to 84, and incidents in dark, unlighted conditions fell from 43 to 27.

Weather

Clear82 (64.1%)
↓-24.8%prior 109
Cloudy13 (10.2%)
↑30.0%prior 10
Snow12 (9.4%)
↑20.0%prior 10
Blowing Snow9 (7.0%)
Rain4 (3.1%)
Severe Winds4 (3.1%)
Fog, smoke, smog2 (1.6%)
Freezing rain/drizzle1 (0.8%)
↓-83.3%prior 6
Other (explain in narrative)1 (0.8%)

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

Lighting

Daylight92 (70.8%)
↑1.1%prior 91
Dark - roadway not lighted27 (20.8%)
↓-37.2%prior 43
Dark - roadway lighted5 (3.8%)
→0.0%prior 5
Dusk4 (3.1%)
Dark - unknown roadway lighting2 (1.5%)

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

Road Surface

Dry84 (65.1%)
↓-24.3%prior 111
Snow23 (17.8%)
↑76.9%prior 13
Wet11 (8.5%)
↑83.3%prior 6
Ice/frost8 (6.2%)
↓-42.9%prior 14
Gravel2 (1.6%)
Slush1 (0.8%)

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 Ford (45 vehicles) and Chevrolet (53 vehicles combined as 'CHEV' and 'CHEVROLET') leading in both 2024 and 2025. Analysis of driver age shows a significant shift; the number of persons aged 16-20 involved in crashes decreased from 32 to 21. In contrast, the 26-34 age group saw a notable increase, rising from 24 persons involved in crashes in 2024 to 38 in 2025.

Top Vehicle Makes (220 vehicles)

1
FORD45 (20.5%)
→0.0%prior 45
2
CHEV38 (17.3%)
→0.0%prior 38
3
CHEVROLET15 (6.8%)
↓-28.6%prior 21
4
GMC10 (4.5%)
→0.0%prior 10
5
DODG9 (4.1%)
↑80.0%prior 5
6
NR9 (4.1%)
↑80.0%prior 5
7
PETERBILT7 (3.2%)
8
RAM6 (2.7%)
9
FREIGHTLINER5 (2.3%)
↓-54.5%prior 11
10
NISSAN5 (2.3%)

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

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

Sex Distribution (126 persons with recorded sex)

Male97 (77.0%)
↑12.8%prior 86
Female29 (23.0%)
↓-42.0%prior 50

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: 154
  • Total persons involved: 225
  • Total vehicles involved: 220

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

ThatCarHitMe.com · An Injuria.ai Company