Yearly Traffic Safety Analysis

118 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Sac County recorded 118 total crashes, a 20.8% decrease from the 149 crashes reported in 2024. During this period, total fatalities decreased from 3 to 2, and the total number of people injured fell from 47 to 37. A notable change was the significant reduction in crashes attributed to driving under the influence, which fell from 8 incidents in 2024 to 2 in 2025.

118

▼ -20.8%was 149

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

37

▼ -21.3%was 47

Persons Injured

2

▼ -33.3%was 3

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

Traffic crashes in Sac County showed a downward trend year-over-year. The total number of crashes decreased from 149 in 2024 to 118 in 2025, representing a 20.8% reduction. This decline was also reflected in the number of people harmed, with total injuries falling by 21.3% from 47 to 37 and fatalities decreasing from 3 to 2.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 3↓-33.3%

0

Other Killed

Prior: 0→0.0%

36

Motorists Injured

Prior: 46↓-21.7%

1

Other Injured

Prior: 1→0.0%

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

Fridays remained the most frequent day for crashes in both periods, though the number of Friday crashes decreased from 34 in 2024 to 22 in 2025. The peak hour for crashes shifted earlier, moving from 7 p.m. in 2024 (10 crashes) to 5 p.m. in 2025 (10 crashes). Overall, the daily distribution of crashes became more uniform in 2025, with a less pronounced peak on Fridays compared to the previous year.

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

The overall severity of crashes decreased from 2024 to 2025. The fatal crash rate fell from 2.01% to 1.69%, with 2 fatal crashes in 2025 compared to 3 in the prior year. The proportion of crashes resulting in any type of injury also declined, with serious injury crashes dropping from 4.0% to 2.5% of the total. Correspondingly, the share of crashes with no reported injuries increased from 68.5% in 2024 to 73.7% in 2025.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.7%
↓-33.3%prior 3
Serious Injury3serious injury crashes2.5%
↓-50.0%prior 6
Minor Injury14minor injury crashes11.9%
↓-30.0%prior 20
Possible Injury12possible injury crashes10.2%
↓-33.3%prior 18
No Injury87no injury crashes73.7%
↓-14.7%prior 102

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, though the count decreased by 22.5% from 40 crashes in 2024 to 31 in 2025. While 'Lost Control' remained a leading cause with a stable count (14 in 2024 vs. 13 in 2025), there were notable shifts in other factors. Crashes attributed to 'Ran off road - straight' increased from 7 to 12, and 'Failure to Yield Right of Way from a stop sign' more than doubled from 4 to 9 incidents. Conversely, crashes involving 'Driver Distraction: Other interior distraction' saw a significant drop from 8 incidents to just 1.

Officer-Reported Primary Contributing Cause

Animal31 (26.3%)↓-22.5%prior 40
Lost Control13 (11%)↓-7.1%prior 14
Ran off road - straight12 (10.2%)↑71.4%prior 7
FTYROW: From stop sign9 (7.6%)
Followed too close7 (5.9%)↑40.0%prior 5
Driving too fast for conditions7 (5.9%)
Other (explain in narrative): Other5 (4.2%)↓-66.7%prior 15
Improper Backing4 (3.4%)
Swerving/Evasive Action4 (3.4%)
Ran off road - left3 (2.5%)↓-72.7%prior 11

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 remained largely consistent year-over-year, mirroring the overall decline in crash totals. Crashes in clear weather and on dry roads decreased in count but represented a stable proportion of total incidents, accounting for approximately half of all crashes in both 2024 and 2025. The proportion of crashes happening in daylight decreased from 49% of all crashes in 2024 to 42% in 2025. The share of crashes on roads affected by snow or ice remained unchanged at 12.7% in both periods.

Weather

Clear61 (67.8%)
↓-21.8%prior 78
Cloudy12 (13.3%)
↓-7.7%prior 13
Snow7 (7.8%)
↑40.0%prior 5
Rain3 (3.3%)
Blowing Snow2 (2.2%)
Fog, smoke, smog2 (2.2%)
Freezing rain/drizzle2 (2.2%)
Other (explain in narrative)1 (1.1%)

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

Lighting

Daylight50 (54.9%)
↓-31.5%prior 73
Dark - roadway not lighted19 (20.9%)
↓-29.6%prior 27
Dawn10 (11.0%)
↑100.0%prior 5
Dusk8 (8.8%)
Dark - roadway lighted4 (4.4%)

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

Road Surface

Dry58 (64.4%)
↓-25.6%prior 78
Snow10 (11.1%)
↑11.1%prior 9
Wet8 (8.9%)
↑60.0%prior 5
Gravel6 (6.7%)
↓-33.3%prior 9
Ice/frost5 (5.6%)
↓-50.0%prior 10
Mud, dirt2 (2.2%)
Slush1 (1.1%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Chevrolet and Ford being the most common in both 2024 and 2025. However, the number of Jeeps involved in crashes decreased from 18 to 7. An analysis of the age of persons involved shows that while most age groups saw a decrease in involvement, the 16-20 age group remained constant with 20 individuals involved in both years, increasing their proportional representation. Conversely, involvement for the 26-34 age group dropped from 41 people in 2024 to 29 in 2025.

Top Vehicle Makes (162 vehicles)

1
CHEV40 (24.7%)
↑8.1%prior 37
2
FORD30 (18.5%)
↓-6.3%prior 32
3
TOYT9 (5.6%)
4
CHEVROLET8 (4.9%)
↓-42.9%prior 14
5
GMC8 (4.9%)
↑33.3%prior 6
6
JEEP7 (4.3%)
↓-61.1%prior 18
7
PTRB5 (3.1%)
8
PETERBILT5 (3.1%)
↓-28.6%prior 7
9
TOYO4 (2.5%)
10
DODG4 (2.5%)
↓-33.3%prior 6

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

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

Sex Distribution (88 persons with recorded sex)

Male58 (65.9%)
↓-20.5%prior 73
Female30 (34.1%)
↓-26.8%prior 41

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: 118
  • Total persons involved: 169
  • Total vehicles involved: 162

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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