Yearly Traffic Safety Analysis

190 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Wright County recorded 190 total crashes, a 5.0% increase from the 181 crashes reported in 2024. While total fatalities remained stable at one, total injuries rose by 27.9% from 43 to 55. A notable year-over-year change was observed in contributing factors, where crashes attributed to 'driving too fast for conditions' more than doubled, increasing from 8 in 2024 to 17 in 2025.

190

▲ 5.0%was 181

Total Crash Events

1

Persons Killed

55

▲ 27.9%was 43

Persons Injured

1

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

Overall, Wright County experienced a 5.0% increase in total crashes, rising from 181 in 2024 to 190 in 2025. This upward trend was more pronounced in crash outcomes, with total injuries increasing by 27.9% from 43 to 55. Fatalities held steady, with one person killed in a crash in both years.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

55

Motorists Injured

Prior: 42↑31.0%

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

The timing of crashes in Wright County shifted between the two periods. In 2025, the peak day for crashes was Thursday with 32 incidents, a change from 2024 when Friday was the peak day with 35 incidents. Similarly, the peak hour for crashes moved earlier in the day, from 6 p.m. in 2024 (18 crashes) to 3 p.m. in 2025 (17 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 number of fatal crashes remained constant at one, with the fatal crash rate per 100 crashes slightly decreasing from 0.55 to 0.53. The distribution of injury crashes shifted; the count of serious injury crashes decreased from 5 to 3. Conversely, crashes resulting in minor injuries increased from 10 to 16, and possible injury crashes rose from 15 to 18.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
→0.0%prior 1
Serious Injury3serious injury crashes1.6%
↓-40.0%prior 5
Minor Injury16minor injury crashes8.4%
↑60.0%prior 10
Possible Injury18possible injury crashes9.5%
↑20.0%prior 15
No Injury152no injury crashes80%
↑1.3%prior 150

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 leading contributing factor in both periods, with the count increasing from 40 in 2024 to 44 in 2025. A significant shift occurred with 'Driving too fast for conditions,' which more than doubled in count from 8 to 17 crashes, moving from the fifth-ranked to the second-ranked factor. In contrast, crashes attributed to 'Ran off road - left' were halved, decreasing from 14 incidents in 2024 to 7 in 2025.

Officer-Reported Primary Contributing Cause

Animal44 (23.2%)↑10.0%prior 40
Driving too fast for conditions17 (8.9%)↑112.5%prior 8
Other (explain in narrative): Other12 (6.3%)↑20.0%prior 10
FTYROW: From stop sign10 (5.3%)↑11.1%prior 9
Improper Backing8 (4.2%)→0.0%prior 8
Lost Control8 (4.2%)↑33.3%prior 6
FTYROW: At uncontrolled intersection8 (4.2%)↑60.0%prior 5
Driver Distraction: Other interior distraction7 (3.7%)↑16.7%prior 6
Ran off road - left7 (3.7%)↓-50.0%prior 14
Ran off road - straight5 (2.6%)

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 proportion of crashes occurring in clear weather and on dry roads decreased from 2024 to 2025. In 2024, 60.2% of crashes happened in clear weather, which fell to 50.0% in 2025. Concurrently, there was an increase in crashes on snow or ice-covered roads, which more than doubled from 15 incidents in 2024 to 34 in 2025.

Weather

Clear95 (64.6%)
↓-12.8%prior 109
Cloudy15 (10.2%)
↑25.0%prior 12
Snow12 (8.2%)
Rain9 (6.1%)
↑28.6%prior 7
Blowing Snow5 (3.4%)
Severe Winds5 (3.4%)
Fog, smoke, smog4 (2.7%)
Freezing rain/drizzle2 (1.4%)

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

Lighting

Daylight103 (68.7%)
→0.0%prior 103
Dark - roadway not lighted20 (13.3%)
↓-4.8%prior 21
Dark - roadway lighted15 (10.0%)
↓-6.3%prior 16
Dusk7 (4.7%)
Dawn4 (2.7%)
Dark - unknown roadway lighting1 (0.7%)

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

Road Surface

Dry90 (60.8%)
↓-16.7%prior 108
Ice/frost17 (11.5%)
↑142.9%prior 7
Snow17 (11.5%)
↑112.5%prior 8
Wet16 (10.8%)
↑45.5%prior 11
Gravel8 (5.4%)

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

Vehicles & Demographics

The demographics of vehicles and persons involved in crashes showed notable changes. Combining 'CHEV' and 'CHEVROLET' records, Chevrolet-branded vehicles were involved in 80 crashes in 2025, overtaking Ford (41 crashes) as the most common make involved in collisions. The age distribution of persons involved also shifted; the 35-44 age group saw its involvement increase from 42 to 58 individuals, becoming the largest group in 2025. In contrast, the 16-20 age group, which was the largest in 2024 with 47 individuals, saw its involvement decrease to 39.

Top Vehicle Makes (314 vehicles)

1
CHEV51 (16.2%)
↑41.7%prior 36
2
FORD41 (13.1%)
↓-8.9%prior 45
3
CHEVROLET29 (9.2%)
↑16.0%prior 25
4
GMC18 (5.7%)
↑5.9%prior 17
5
DODG14 (4.5%)
↓-12.5%prior 16
6
JEEP13 (4.1%)
↑44.4%prior 9
7
BUIC12 (3.8%)
↑9.1%prior 11
8
TOYT11 (3.5%)
↓-21.4%prior 14
9
TOYOTA9 (2.9%)
↑50.0%prior 6
10
CHRY8 (2.5%)

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

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

Sex Distribution (178 persons with recorded sex)

Male110 (61.8%)
↑5.8%prior 104
Female68 (38.2%)
↑9.7%prior 62

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: 190
  • Total persons involved: 328
  • Total vehicles involved: 314

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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