Yearly Traffic Safety Analysis

366 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Plymouth County recorded 366 total crashes, a 3.2% decrease from the 378 crashes reported in 2024. Despite the overall reduction in collisions, the number of people injured increased by 11.3%, rising from 115 in the prior year to 128 in the current year, while fatalities decreased from 6 to 5.

366

▼ -3.2%was 378

Total Crash Events

5

▼ -16.7%was 6

Persons Killed

128

▲ 11.3%was 115

Persons Injured

5

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 crashes in Plymouth County showed a slight decline year-over-year, with total incidents falling by 3.2% from 378 in 2024 to 366 in 2025. This period saw a divergence in outcomes, as the number of fatalities decreased from 6 to 5, while the total count of injuries rose by 11.3% from 115 to 128.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 6↓-16.7%

4

Cyclists Injured

Prior: 3↑33.3%

124

Motorists Injured

Prior: 111↑11.7%

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 shifted between the two periods. The peak day for crashes moved from Friday (78 crashes) in 2024 to Wednesday (68 crashes) in 2025. The 4 p.m. hour remained the single busiest hour for crashes in both periods, though the count in that hour decreased slightly from 35 to 33. Notably, Friday crashes decreased from 78 to 49, while Wednesday remained a high-volume day with 68 crashes in both years.

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 number of fatal crashes remained unchanged at 5 incidents in both 2025 and 2024, which represented a slight increase in the fatal crash rate from 1.32% to 1.37% of all crashes. Crashes resulting in serious injuries decreased in both count and proportion, falling from 11 incidents (2.9% of total) to 7 incidents (1.9% of total). Conversely, crashes involving possible injuries increased from 37 to 39, and their share of all crashes rose from 9.8% to 10.7%.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.4%
→0.0%prior 5
Serious Injury7serious injury crashes1.9%
↓-36.4%prior 11
Minor Injury41minor injury crashes11.2%
↓-4.7%prior 43
Possible Injury39possible injury crashes10.7%
↑5.4%prior 37
No Injury274no injury crashes74.9%
↓-2.8%prior 282

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 by 17.8% from 90 in 2024 to 74 in 2025. 'Lost Control' became the second-most cited factor, with its count increasing from 22 to 25 incidents. A notable increase was seen in crashes attributed to 'Driver Distraction: Other interior distraction,' which grew by 58.3% from 12 to 19 incidents, moving it into the top five factors for the current period.

Officer-Reported Primary Contributing Cause

Animal74 (20.2%)↓-17.8%prior 90
Lost Control25 (6.8%)↑13.6%prior 22
Followed too close24 (6.6%)↓-4.0%prior 25
FTYROW: From stop sign20 (5.5%)↓-13.0%prior 23
Driver Distraction: Other interior distraction19 (5.2%)↑58.3%prior 12
Driving too fast for conditions18 (4.9%)↓-21.7%prior 23
Ran Stop Sign15 (4.1%)↓-11.8%prior 17
Other (explain in narrative): Other15 (4.1%)↑7.1%prior 14
Ran off road - left14 (3.8%)↓-6.7%prior 15
Ran off road - straight12 (3.3%)↓-40.0%prior 20

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 majority of crashes in both periods occurred in clear weather and daylight conditions. Crashes on dry road surfaces increased from 212 to 235 year-over-year, while incidents on icy or frosty roads decreased from 25 to 12. Incidents during snowy weather increased from 10 to 15, and crashes on snow-covered roads rose from 21 to 26. Overall, the distribution of crashes across conditions remained broadly similar, with most incidents happening in favorable conditions.

Weather

Clear220 (73.3%)
↑2.3%prior 215
Cloudy41 (13.7%)
→0.0%prior 41
Snow15 (5.0%)
↑50.0%prior 10
Rain12 (4.0%)
↑100.0%prior 6
Fog, smoke, smog4 (1.3%)
Freezing rain/drizzle3 (1.0%)
↓-57.1%prior 7
Severe Winds3 (1.0%)
Blowing Snow2 (0.7%)

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

Lighting

Daylight210 (68.9%)
↑1.0%prior 208
Dark - roadway not lighted50 (16.4%)
↓-5.7%prior 53
Dark - roadway lighted22 (7.2%)
↑37.5%prior 16
Dawn11 (3.6%)
↑22.2%prior 9
Dusk10 (3.3%)
↑25.0%prior 8
Dark - unknown roadway lighting2 (0.7%)

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

Road Surface

Dry235 (77.8%)
↑10.8%prior 212
Snow26 (8.6%)
↑23.8%prior 21
Wet20 (6.6%)
↑5.3%prior 19
Ice/frost12 (4.0%)
↓-52.0%prior 25
Gravel7 (2.3%)
↓-30.0%prior 10
Slush2 (0.7%)

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 Ford and Chevrolet models being the most frequent in both years. The number of Ford vehicles involved decreased from 109 to 90, while Chevrolet vehicles (listed as 'CHEV' and 'CHEVROLET') saw a combined increase in involvement from 121 to 135. Regarding driver and passenger demographics, there was a shift in the most-involved age groups; the 35-44 age group's involvement increased from 83 to 100 persons, while the 26-34 age group's involvement decreased from 105 to 92 persons.

Top Vehicle Makes (574 vehicles)

1
CHEV108 (18.8%)
↑22.7%prior 88
2
FORD90 (15.7%)
↓-17.4%prior 109
3
GMC28 (4.9%)
↓-3.4%prior 29
4
CHEVROLET27 (4.7%)
↓-18.2%prior 33
5
JEEP23 (4%)
↓-8.0%prior 25
6
HOND22 (3.8%)
↑69.2%prior 13
7
BUIC19 (3.3%)
↑90.0%prior 10
8
KIA15 (2.6%)
↑15.4%prior 13
9
CHRY14 (2.4%)
↑40.0%prior 10
10
DODG14 (2.4%)
↓-41.7%prior 24

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

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

Sex Distribution (375 persons with recorded sex)

Male224 (59.7%)
↑8.2%prior 207
Female151 (40.3%)
↑0.7%prior 150

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: 366
  • Total persons involved: 615
  • Total vehicles involved: 574

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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