Yearly Traffic Safety Analysis

124 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Emmet County, total traffic crashes decreased from 181 in 2024 to 124 in 2025, representing a 31.5% reduction. The most significant year-over-year change was a 54.2% drop in crashes involving animals, which fell from 48 incidents to 22.

124

▼ -31.5%was 181

Total Crash Events

0

Persons Killed

35

▼ -37.5%was 56

Persons Injured

0

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 traffic safety trends in Emmet County improved year-over-year. The total number of crashes fell by 31.5%, from 181 in the prior period to 124 in the current period. Correspondingly, total injuries declined by 37.5% from 56 to 35, while fatalities remained at zero in both years.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

2

Cyclists Injured

Prior: 0→%

33

Motorists Injured

Prior: 56↓-41.1%

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 significantly between the two periods. In 2025, the peak day for crashes was Tuesday with 23 incidents, and the peak hour was 5 p.m. with 12 incidents. This contrasts with the prior year, when Friday was the peak day (36 crashes) and the morning commute hour of 7 a.m. was the peak time (20 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 saw a general improvement year-over-year, with zero fatal crashes recorded in either period. The number of serious injury crashes decreased from 5 to 2, and minor injury crashes fell from 23 to 10. While the count of possible injury crashes increased from 12 to 15, the overall number of crashes involving any injury dropped from 40 in 2024 to 27 in 2025.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes1.6%
↓-60.0%prior 5
Minor Injury10minor injury crashes8.1%
↓-56.5%prior 23
Possible Injury15possible injury crashes12.1%
↑25.0%prior 12
No Injury97no injury crashes78.2%
↓-31.2%prior 141

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 with animals remained the leading contributing factor in both periods, but the count of such incidents dropped by 54.2% from 48 to 22. Crashes attributed to 'Driving too fast for conditions' also decreased from 16 to 12 incidents. Conversely, crashes attributed to 'Improper Backing' increased by 125%, from 4 to 9 incidents, making it the third most common factor in the current period.

Officer-Reported Primary Contributing Cause

Animal22 (17.7%)↓-54.2%prior 48
Driving too fast for conditions12 (9.7%)↓-25.0%prior 16
Improper Backing9 (7.3%)
Operating vehicle in an reckless, erratic, careless, negligent manner7 (5.6%)↓-12.5%prior 8
Other (explain in narrative): Other7 (5.6%)↓-36.4%prior 11
FTYROW: From stop sign6 (4.8%)→0.0%prior 6
Lost Control5 (4%)→0.0%prior 5
Driver Distraction: Other interior distraction5 (4%)
FTYROW: Other (explain in narrative)4 (3.2%)
FTYROW: From yield sign4 (3.2%)

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

Road & Environmental Conditions

While overall crashes declined, the proportion of incidents occurring in favorable conditions increased. Crashes in daylight rose from constituting 49.2% of the total in the prior year to 63.7% in the current year. Similarly, the share of crashes on dry roads increased from 58.6% to 64.5%. Incidents on roads with snow or ice decreased in absolute numbers, from 23 in the prior year to 15 in the current year.

Weather

Clear86 (76.8%)
↓-18.1%prior 105
Cloudy12 (10.7%)
↓-25.0%prior 16
Snow6 (5.4%)
↓-40.0%prior 10
Blowing Snow2 (1.8%)
Freezing rain/drizzle2 (1.8%)
Severe Winds2 (1.8%)
Fog, smoke, smog1 (0.9%)
↓-90.0%prior 10
Rain1 (0.9%)
↓-85.7%prior 7

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

Lighting

Daylight79 (70.5%)
↓-11.2%prior 89
Dark - roadway not lighted17 (15.2%)
↓-52.8%prior 36
Dark - roadway lighted12 (10.7%)
↓-29.4%prior 17
Dawn3 (2.7%)
Dusk1 (0.9%)
↓-80.0%prior 5

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

Road Surface

Dry80 (71.4%)
↓-24.5%prior 106
Snow11 (9.8%)
↑10.0%prior 10
Gravel9 (8.0%)
Wet8 (7.1%)
↓-55.6%prior 18
Ice/frost3 (2.7%)
↓-75.0%prior 12
Slush1 (0.9%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Chevrolet and Ford, remained consistent across both years, with their counts decreasing in line with the overall downward trend. The age demographics of persons involved in crashes shifted; the 16-20 age group became the most represented in the current period with 34 individuals, replacing the 65+ age group from the prior year. The number of individuals aged 65 and older involved in crashes decreased from 50 to 32.

Top Vehicle Makes (194 vehicles)

1
CHEV39 (20.1%)
↓-26.4%prior 53
2
FORD30 (15.5%)
↓-28.6%prior 42
3
CHEVROLET14 (7.2%)
↓-39.1%prior 23
4
GMC13 (6.7%)
↓-7.1%prior 14
5
DODG11 (5.7%)
↑37.5%prior 8
6
TOYO11 (5.7%)
↓-26.7%prior 15
7
BUIC6 (3.1%)
↓-50.0%prior 12
8
JEEP6 (3.1%)
↓-45.5%prior 11
9
NR6 (3.1%)
↓-45.5%prior 11
10
RAM5 (2.6%)
→0.0%prior 5

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

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

Sex Distribution (110 persons with recorded sex)

Male63 (57.3%)
↓-27.6%prior 87
Female47 (42.7%)
↓-27.7%prior 65

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: 124
  • Total persons involved: 204
  • Total vehicles involved: 194

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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