Yearly Traffic Safety Analysis

979 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Cerro Gordo County recorded 979 total crashes, a 2.3% decrease from the 1,002 crashes documented in 2024. This slight downturn in overall incidents was accompanied by a significant positive shift in outcomes. The most notable year-over-year change was a substantial decrease in traffic fatalities, which fell from 7 in the prior period to 2 in the current period.

979

▼ -2.3%was 1,002

Total Crash Events

2

▼ -71.4%was 7

Persons Killed

237

▼ -5.2%was 250

Persons Injured

2

▼ -71.4%was 7

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 traffic safety trends in Cerro Gordo County showed improvement from 2024 to 2025. Total crashes decreased by 2.3%, from 1,002 to 979. This downward trend was also reflected in crash outcomes, with total injuries declining by 5.2% from 250 to 237, and total fatalities decreasing from 7 to 2.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 2↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 5↓-60.0%

7

Pedestrians Injured

Prior: 3↑133.3%

6

Cyclists Injured

Prior: 7↓-14.3%

224

Motorists Injured

Prior: 240↓-6.7%

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 temporal patterns of crashes showed some shifts between the two periods. The peak day for crashes moved from Friday (185 crashes) in 2024 to Wednesday (173 crashes) in 2025. However, the peak hour for collisions remained consistent at 3 p.m. in both years, accounting for 74 crashes in the current period compared to 80 in the prior 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 decreased notably year-over-year, driven by a sharp drop in fatal incidents. The number of fatal crashes fell from 7 in 2024 to 2 in 2025, reducing the fatal crash rate from 0.7% to 0.2% of all crashes. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) remained relatively stable, accounting for 21.1% of all crashes in 2025 compared to 21.3% in 2024.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.2%
↓-71.4%prior 7
Serious Injury13serious injury crashes1.3%
↓-18.8%prior 16
Minor Injury67minor injury crashes6.8%
↓-6.9%prior 72
Possible Injury127possible injury crashes13%
↑1.6%prior 125
No Injury770no injury crashes78.7%
↓-1.5%prior 782

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 leading contributing factor in both periods, though the count of such incidents decreased from 208 in 2024 to 181 in 2025. 'Followed too close' became the second-most common factor in 2025, with its count increasing from 84 to 90. Crashes attributed to 'Ran Traffic Signal' saw a notable increase in count from 26 to 36, while incidents involving 'Ran off road - left' decreased from 62 to 49.

Officer-Reported Primary Contributing Cause

Animal181 (18.5%)↓-13.0%prior 208
Followed too close90 (9.2%)↑7.1%prior 84
FTYROW: From stop sign59 (6%)↑7.3%prior 55
Other (explain in narrative): Other52 (5.3%)↓-8.8%prior 57
Ran off road - left49 (5%)↓-21.0%prior 62
Driving too fast for conditions40 (4.1%)↓-2.4%prior 41
Ran Traffic Signal36 (3.7%)↑38.5%prior 26
Driver Distraction: Other interior distraction35 (3.6%)↓-22.2%prior 45
FTYROW: Making left turn32 (3.3%)↑10.3%prior 29
Lost Control27 (2.8%)↑3.8%prior 26

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 distribution of crashes across lighting conditions remained largely unchanged year-over-year, with approximately 61% of incidents in 2025 occurring in daylight. However, there was a noticeable increase in crashes occurring on compromised road surfaces; collisions on snowy surfaces rose from 39 to 67. Correspondingly, the share of crashes on dry road surfaces decreased from 63.6% in 2024 to 62.6% in 2025.

Weather

Clear572 (69.5%)
↓-1.7%prior 582
Cloudy145 (17.6%)
↓-6.5%prior 155
Snow32 (3.9%)
↑88.2%prior 17
Rain29 (3.5%)
↑7.4%prior 27
Blowing Snow17 (2.1%)
↑240.0%prior 5
Severe Winds10 (1.2%)
Freezing rain/drizzle9 (1.1%)
↓-40.0%prior 15
Fog, smoke, smog5 (0.6%)
↓-61.5%prior 13
Sleet, hail2 (0.2%)
Other (explain in narrative)2 (0.2%)

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

Lighting

Daylight601 (72.1%)
↑0.3%prior 599
Dark - roadway lighted97 (11.6%)
↑2.1%prior 95
Dark - roadway not lighted92 (11.0%)
↑9.5%prior 84
Dawn17 (2.0%)
↓-15.0%prior 20
Dusk14 (1.7%)
↓-36.4%prior 22
Dark - unknown roadway lighting13 (1.6%)
↑44.4%prior 9

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

Road Surface

Dry613 (74.6%)
↓-3.8%prior 637
Wet84 (10.2%)
↑15.1%prior 73
Snow67 (8.2%)
↑71.8%prior 39
Ice/frost43 (5.2%)
↓-18.9%prior 53
Gravel6 (0.7%)
↓-45.5%prior 11
Other (explain in narrative)5 (0.6%)
Slush3 (0.4%)
↓-50.0%prior 6
Mud, dirt1 (0.1%)

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 (309) and Chevrolet (277) holding the top two spots in 2025, both seeing a slight increase in counts from the prior year. The representation of persons by age group also showed stability, although the 65+ age group saw a notable increase in involvement, rising from 262 individuals in 2024 to 287 in 2025. Conversely, the number of persons in the 16-20 age group involved in crashes slightly decreased from 210 to 208.

Top Vehicle Makes (1,660 vehicles)

1
FORD309 (18.6%)
↑5.5%prior 293
2
CHEV277 (16.7%)
↑1.8%prior 272
3
GMC74 (4.5%)
↑8.8%prior 68
4
DODG66 (4%)
↓-8.3%prior 72
5
NISS64 (3.9%)
↑1.6%prior 63
6
JEEP62 (3.7%)
↓-12.7%prior 71
7
CHEVROLET60 (3.6%)
↓-11.8%prior 68
8
TOYT58 (3.5%)
↓-40.8%prior 98
9
HOND58 (3.5%)
↓-9.4%prior 64
10
NR41 (2.5%)
↑5.1%prior 39

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

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

Sex Distribution (1,102 persons with recorded sex)

Male618 (56.1%)
↑9.8%prior 563
Female484 (43.9%)
↑4.1%prior 465

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: 979
  • Total persons involved: 1,717
  • Total vehicles involved: 1,660

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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