Yearly Traffic Safety Analysis

235 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Hardin County, total traffic crashes decreased by 21.1% from 298 incidents in 2024 to 235 in 2025. This downward trend was accompanied by a significant reduction in crash severity. The most notable year-over-year shift was the decrease in total fatalities from four to one and a 38.8% drop in total injuries.

235

▼ -21.1%was 298

Total Crash Events

1

▼ -75.0%was 4

Persons Killed

52

▼ -38.8%was 85

Persons Injured

1

▼ -66.7%was 3

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 Hardin County indicates a significant downward trend year-over-year. The total number of crashes fell from 298 to 235, a 21.1% decrease. Similarly, the number of people killed in crashes dropped from four in 2024 to one in 2025, and the number of people injured decreased from 85 to 52.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 4↓-75.0%

52

Motorists Injured

Prior: 84↓-38.1%

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 shifted between the two periods. In 2025, the peak days for crashes were Monday, Thursday, and Friday, each with 39 incidents, a change from 2024 when Tuesday was the peak day with 58 crashes. The peak time for crashes also shifted slightly, from 3 p.m. in 2024 (28 crashes) to a tie between 2 p.m. and 5 p.m. in 2025 (20 crashes each).

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

Crash severity decreased from 2024 to 2025. The fatal crash rate fell from 1.01% to 0.43%, with fatal crashes dropping from three to one. The proportion of crashes resulting in any injury (fatal, serious, minor, or possible) also saw a slight decrease, from 24.5% of all crashes in 2024 to 20.9% in 2025. Specifically, serious injury crashes decreased from five to two.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
↓-66.7%prior 3
Serious Injury2serious injury crashes0.9%
↓-60.0%prior 5
Minor Injury22minor injury crashes9.4%
↓-35.3%prior 34
Possible Injury24possible injury crashes10.2%
↓-25.0%prior 32
No Injury186no injury crashes79.1%
↓-17.0%prior 224

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 top contributing factor in both periods, though the count of these incidents decreased by 38.9% from 108 in 2024 to 66 in 2025. The second most common factor in 2025 was 'Ran off road - left' with 19 crashes. Notably, crashes attributed to 'Lost Control' increased from 5 to 13 incidents year-over-year, while crashes involving 'Failure to yield from a stop sign' decreased from 17 to 4.

Officer-Reported Primary Contributing Cause

Animal66 (28.1%)↓-38.9%prior 108
Ran off road - left19 (8.1%)↑137.5%prior 8
Other (explain in narrative): Other16 (6.8%)↓-20.0%prior 20
Ran off road - straight15 (6.4%)↑15.4%prior 13
Lost Control13 (5.5%)↑160.0%prior 5
Driving too fast for conditions12 (5.1%)↓-7.7%prior 13
Followed too close9 (3.8%)↓-35.7%prior 14
Operating vehicle in an reckless, erratic, careless, negligent manner8 (3.4%)↑33.3%prior 6
Improper Backing7 (3%)↑40.0%prior 5
Ran Stop Sign6 (2.6%)↓-53.8%prior 13

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 distribution of crashes across lighting and road surface conditions showed some shifts year-over-year. While the majority of crashes in both periods occurred in daylight and on dry roads, the number of crashes on roads with ice or frost more than tripled, increasing from 10 in 2024 to 31 in 2025. Similarly, crashes during snowy weather increased from 3 to 9 incidents.

Weather

Clear134 (70.9%)
↓-14.1%prior 156
Cloudy25 (13.2%)
↓-24.2%prior 33
Snow9 (4.8%)
Severe Winds6 (3.2%)
Freezing rain/drizzle5 (2.6%)
→0.0%prior 5
Rain5 (2.6%)
Fog, smoke, smog3 (1.6%)
↓-62.5%prior 8
Blowing Snow2 (1.1%)

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

Lighting

Daylight124 (65.3%)
↓-17.9%prior 151
Dark - roadway not lighted41 (21.6%)
↓-6.8%prior 44
Dark - roadway lighted13 (6.8%)
↑8.3%prior 12
Dusk6 (3.2%)
Dawn3 (1.6%)
↓-40.0%prior 5
Dark - unknown roadway lighting3 (1.6%)

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

Road Surface

Dry127 (67.2%)
↓-23.5%prior 166
Ice/frost31 (16.4%)
↑210.0%prior 10
Wet15 (7.9%)
↓-6.3%prior 16
Snow9 (4.8%)
↓-10.0%prior 10
Gravel5 (2.6%)
↓-54.5%prior 11
Slush2 (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 relatively consistent year-over-year. Chevrolet and Ford were the top two most frequently involved makes in both 2025 (65 and 60 vehicles, respectively) and 2024 (77 and 66 vehicles). The age distribution of persons involved in crashes also showed stability, with no significant shifts in the proportional representation of any single age group between the two periods.

Top Vehicle Makes (328 vehicles)

1
CHEV65 (19.8%)
↓-15.6%prior 77
2
FORD60 (18.3%)
↓-9.1%prior 66
3
DODG18 (5.5%)
↓-5.3%prior 19
4
JEEP17 (5.2%)
↓-10.5%prior 19
5
GMC13 (4%)
↓-43.5%prior 23
6
CHEVROLET12 (3.7%)
↓-47.8%prior 23
7
BUIC10 (3%)
↑11.1%prior 9
8
TOYT10 (3%)
↑42.9%prior 7
9
NISS9 (2.7%)
↓-47.1%prior 17
10
NR8 (2.4%)
↑60.0%prior 5

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

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

Sex Distribution (193 persons with recorded sex)

Male111 (57.5%)
↓-19.0%prior 137
Female82 (42.5%)
↑5.1%prior 78

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: 235
  • Total persons involved: 337
  • Total vehicles involved: 328

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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