Yearly Traffic Safety Analysis

136 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Fremont County, total crashes decreased from 159 in 2024 to 136 in 2025, a 14.5% reduction. While overall collisions and fatalities fell, the number of people injured increased from 56 to 61. One of the most notable shifts was in crashes involving a suspected DUI, which rose from 4 to 7 incidents year-over-year.

136

▼ -14.5%was 159

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

61

▲ 8.9%was 56

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

Crash data for Fremont County indicates a downward trend in the total number of collisions, with 23 fewer crashes in 2025 compared to 2024, representing a 14.5% decrease. Despite the drop in total and fatal crashes (from 2 to 1), the number of people injured rose by 8.9%, from 56 to 61.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 0→%

60

Motorists Injured

Prior: 56↑7.1%

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 temporal patterns of crashes showed some shifts between the two periods. While Friday remained the peak day for crashes in both 2024 (32 crashes) and 2025 (25 crashes), Sunday saw its crash count increase from 16 to 24, becoming the second-busiest day in the current year. The peak hour for collisions shifted from 8 PM in the prior year (15 crashes) to a three-way tie at 4 PM, 5 PM, and 7 PM in the current year, each with 10 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

Year-over-year, the severity of crashes in Fremont County shifted. The number of fatal crashes decreased from 2 in 2024 to 1 in 2025, with the fatal crash rate dropping from 1.3% to 0.7% of all crashes. However, the proportion of crashes resulting in serious injuries more than doubled, rising from 4.4% (7 crashes) to 9.6% (13 crashes). Consequently, the share of crashes with no injuries decreased from 69.2% to 63.2%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
↓-50.0%prior 2
Serious Injury13serious injury crashes9.6%
↑85.7%prior 7
Minor Injury15minor injury crashes11%
↓-34.8%prior 23
Possible Injury21possible injury crashes15.4%
↑23.5%prior 17
No Injury86no injury crashes63.2%
↓-21.8%prior 110

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 an animal remained the leading contributing factor in both periods, though the count decreased by 36.2% from 47 incidents in 2024 to 30 in 2025. 'Lost Control' was the second most common factor in both years, with a relatively stable count of 17 and 16 crashes, respectively. Notably, incidents where the driver was 'Driving too fast for conditions' increased from 8 to 11, and crashes involving 'Ran Stop Sign' increased from 1 to 5.

Officer-Reported Primary Contributing Cause

Animal30 (22.1%)↓-36.2%prior 47
Lost Control16 (11.8%)↓-5.9%prior 17
Ran off road - straight12 (8.8%)↓-7.7%prior 13
Driving too fast for conditions11 (8.1%)↑37.5%prior 8
Followed too close10 (7.4%)↓-28.6%prior 14
Operating vehicle in an reckless, erratic, careless, negligent manner9 (6.6%)↑80.0%prior 5
FTYROW: From stop sign9 (6.6%)↑80.0%prior 5
Ran Stop Sign5 (3.7%)
Driver Distraction: Inattentive/lost in thought4 (2.9%)
Ran off road - left4 (2.9%)↓-33.3%prior 6

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 saw a notable shift towards clearer conditions year-over-year. The number of crashes during daylight hours increased from 74 to 84, raising their share of total crashes from 46.5% in 2024 to 61.8% in 2025. Similarly, while the absolute number of crashes on dry roads remained unchanged at 87, their proportion grew from 54.7% to 64.0% of all incidents. Crashes in clear weather were the most common scenario in both years, with 83 incidents in 2024 and 81 in 2025.

Weather

Clear81 (75.0%)
↓-2.4%prior 83
Cloudy12 (11.1%)
↓-29.4%prior 17
Snow6 (5.6%)
Rain4 (3.7%)
↓-33.3%prior 6
Blowing Snow3 (2.8%)
Severe Winds2 (1.9%)

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

Lighting

Daylight84 (75.7%)
↑13.5%prior 74
Dark - roadway not lighted19 (17.1%)
↓-32.1%prior 28
Dawn3 (2.7%)
Dark - roadway lighted2 (1.8%)
↓-60.0%prior 5
Dusk2 (1.8%)
↓-77.8%prior 9
Dark - unknown roadway lighting1 (0.9%)
↓-83.3%prior 6

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

Road Surface

Dry87 (79.8%)
→0.0%prior 87
Snow9 (8.3%)
↑50.0%prior 6
Wet8 (7.3%)
↓-20.0%prior 10
Ice/frost3 (2.8%)
↓-57.1%prior 7
Gravel2 (1.8%)
↓-71.4%prior 7

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

Vehicles & Demographics

An analysis of vehicles and persons involved shows changes in both make and age demographics. Ford and Chevrolet remained the top two vehicle makes involved in crashes, though the count for both decreased; Ford-involved vehicles dropped from 36 to 24, and Chevrolet-involved vehicles (combining 'CHEVROLET' and 'CHEV') fell from 41 to 35. The age distribution of persons in crashes shifted notably, with the 65+ age group becoming the most represented group in 2025 (35 persons), up from 32 in the prior year. In contrast, the 35-44 age group, which was the most represented in 2024 with 41 individuals, saw its involvement decrease to 18 people in 2025.

Top Vehicle Makes (190 vehicles)

1
FORD24 (12.6%)
↓-33.3%prior 36
2
CHEVROLET19 (10%)
↓-13.6%prior 22
3
CHEV16 (8.4%)
↓-15.8%prior 19
4
GMC11 (5.8%)
↑37.5%prior 8
5
JEEP9 (4.7%)
6
FREIGHTLINER8 (4.2%)
↓-27.3%prior 11
7
TOYOTA7 (3.7%)
8
PETERBILT6 (3.2%)
9
KENWORTH5 (2.6%)
10
HYUN5 (2.6%)

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

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

Sex Distribution (102 persons with recorded sex)

Male71 (69.6%)
↓-9.0%prior 78
Female31 (30.4%)
↓-8.8%prior 34

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: 136
  • Total persons involved: 205
  • Total vehicles involved: 190

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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