Yearly Traffic Safety Analysis

239 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Carroll County, total crashes increased by 5.3% from 227 in 2024 to 239 in 2025. While total fatalities remained stable at 4, the most significant year-over-year change was a 150% increase in crashes involving a driver under the influence (DUI), which rose from 6 to 15 incidents.

239

▲ 5.3%was 227

Total Crash Events

4

Persons Killed

86

▲ 17.8%was 73

Persons Injured

2

▼ -33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (4) 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

Crash trends in Carroll County show an increase year-over-year. Total crashes rose from 227 in 2024 to 239 in 2025, an increase of 5.3%. This was accompanied by a 17.8% rise in total injuries, from 73 to 86, while fatalities held steady at 4 for both periods.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 4→0.0%

1

Pedestrians Injured

Prior: 3↓-66.7%

3

Cyclists Injured

Prior: 1↑200.0%

82

Motorists Injured

Prior: 69↑18.8%

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 in Carroll County remained broadly consistent year-over-year. Friday continued to be the peak day for crashes, with incidents increasing from 45 to 54. The 3 PM hour also remained the peak time for collisions in both periods, with 29 crashes in 2025 compared to 30 in 2024. While the primary peaks were stable, crashes on Mondays increased from 37 to 44, while Wednesday crashes decreased from 43 to 32.

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

While total injuries rose, the severity of crashes shifted slightly. The rate of fatal crashes decreased from 1.32% of all crashes in 2024 to 0.84% in 2025, corresponding to a drop from 3 to 2 fatal incidents. Crashes resulting in minor injuries saw a notable increase, rising from 19 incidents (an 8.4% share) to 25 incidents (a 10.5% share). Conversely, the share of crashes with no injuries decreased slightly from 71.8% to 70.7%.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.8%
↓-33.3%prior 3
Serious Injury6serious injury crashes2.5%
↓-14.3%prior 7
Minor Injury25minor injury crashes10.5%
↑31.6%prior 19
Possible Injury37possible injury crashes15.5%
↑5.7%prior 35
No Injury169no injury crashes70.7%
↑3.7%prior 163

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

The leading contributing factors for crashes saw some significant shifts year-over-year. 'Followed too close' became the top factor in 2025 with 25 incidents, an increase of 78.6% from 14 incidents in 2024 when it was the third-ranked cause. Crashes attributed to 'Ran off road - left' doubled from 9 to 18 incidents. 'Failure to yield from a stop sign' increased from 20 to 23 crashes but moved from the top-ranked cause in 2024 to second place in 2025. 'Ran Stop Sign' also saw an increase, rising from 13 to 18 incidents.

Officer-Reported Primary Contributing Cause

Followed too close25 (10.5%)↑78.6%prior 14
FTYROW: From stop sign23 (9.6%)↑15.0%prior 20
Ran Stop Sign18 (7.5%)↑38.5%prior 13
Ran off road - left18 (7.5%)↑100.0%prior 9
Driving too fast for conditions14 (5.9%)↑16.7%prior 12
Improper Backing12 (5%)↓-7.7%prior 13
Driver Distraction: Other interior distraction12 (5%)↑20.0%prior 10
Other (explain in narrative): Other12 (5%)↓-20.0%prior 15
Made improper turn9 (3.8%)↓-18.2%prior 11
Operating vehicle in an reckless, erratic, careless, negligent manner8 (3.3%)→0.0%prior 8

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 environmental conditions at the time of crashes were largely similar between 2024 and 2025. In both periods, approximately 70% of collisions occurred in clear weather on dry roads. The proportion of crashes happening in daylight increased from 70.0% in 2024 to 74.1% in 2025. Correspondingly, crashes in dark but lighted conditions decreased from 31 incidents to 23.

Weather

Clear169 (72.2%)
↑7.0%prior 158
Cloudy34 (14.5%)
↓-5.6%prior 36
Rain9 (3.8%)
Snow8 (3.4%)
↓-38.5%prior 13
Fog, smoke, smog4 (1.7%)
Blowing Snow4 (1.7%)
Freezing rain/drizzle3 (1.3%)
Severe Winds3 (1.3%)

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

Lighting

Daylight177 (75.0%)
↑11.3%prior 159
Dark - roadway lighted23 (9.7%)
↓-25.8%prior 31
Dark - roadway not lighted22 (9.3%)
↑4.8%prior 21
Dusk8 (3.4%)
↑60.0%prior 5
Dawn3 (1.3%)
Dark - unknown roadway lighting3 (1.3%)

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

Road Surface

Dry170 (72.3%)
↑5.6%prior 161
Wet26 (11.1%)
↑8.3%prior 24
Snow23 (9.8%)
↑43.8%prior 16
Ice/frost9 (3.8%)
↓-25.0%prior 12
Gravel7 (3.0%)
↓-22.2%prior 9

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

Vehicles & Demographics

Analysis of vehicles and persons involved shows shifts in makes and age groups. Chevrolet (including 'CHEV' and 'CHEVROLET' codes) remained the most common make, with involvement increasing from 109 to 122 vehicles. Conversely, Ford vehicles were involved in fewer crashes, dropping from 81 to 63. Regarding persons involved, there was a notable decrease in the 45-54 age group, which fell from 55 individuals in 2024 to 34 in 2025. Meanwhile, the number of individuals in the 16-20 age group involved in crashes increased from 62 to 72.

Top Vehicle Makes (426 vehicles)

1
CHEV95 (22.3%)
↑9.2%prior 87
2
FORD63 (14.8%)
↓-22.2%prior 81
3
JEEP29 (6.8%)
↑52.6%prior 19
4
CHEVROLET27 (6.3%)
↑22.7%prior 22
5
TOYT16 (3.8%)
↑23.1%prior 13
6
BUIC15 (3.5%)
↑7.1%prior 14
7
DODG15 (3.5%)
↓-21.1%prior 19
8
GMC14 (3.3%)
↑27.3%prior 11
9
CHRY13 (3.1%)
↑160.0%prior 5
10
NISS12 (2.8%)
↑140.0%prior 5

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

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

Sex Distribution (293 persons with recorded sex)

Male169 (57.7%)
↑9.0%prior 155
Female124 (42.3%)
↓-3.1%prior 128

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: 239
  • Total persons involved: 448
  • Total vehicles involved: 426

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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