Yearly Traffic Safety Analysis

185 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Madison County, total traffic crashes decreased by 8.0% from 201 in 2024 to 185 in 2025. Despite the overall reduction in collisions, the number of fatalities resulting from these incidents increased from 3 to 5. The most notable shift in contributing factors was a 20% rise in crashes involving animals, which grew from 50 to 60 incidents year-over-year.

185

▼ -8.0%was 201

Total Crash Events

5

▲ 66.7%was 3

Persons Killed

46

▼ -24.6%was 61

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

The overall trend shows a decrease in total traffic incidents and related injuries, but a concerning rise in fatalities. Total crashes fell by 8.0% from 201 to 185, and total injuries dropped by 24.6% from 61 to 46. In contrast, fatalities increased by 66.7%, rising from 3 in the prior year to 5 in the current year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 2↑150.0%

1

Pedestrians Injured

Prior: 0→%

2

Cyclists Injured

Prior: 0→%

43

Motorists Injured

Prior: 61↓-29.5%

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 shifted year-over-year. The prior period had a distinct peak day on Monday with 42 crashes, whereas the current period saw a more distributed pattern with Monday, Tuesday, and Friday each recording a peak of 29 crashes. The single busiest hour for collisions also shifted slightly later, from 4 p.m. in the prior year (20 crashes) to 5 p.m. in the current year (18 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

While the number of fatal crashes remained unchanged at 3 for both periods, the number of people killed in those crashes increased from 3 to 5. The proportion of crashes involving any level of injury (Serious, Minor, or Possible) decreased from 24.4% of all crashes in the prior year to 22.2% in the current year. Correspondingly, the share of crashes resulting in no injuries increased from 74.1% to 76.2%.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.6%
→0.0%prior 3
Serious Injury5serious injury crashes2.7%
→0.0%prior 5
Minor Injury17minor injury crashes9.2%
↓-37.0%prior 27
Possible Injury19possible injury crashes10.3%
↑11.8%prior 17
No Injury141no injury crashes76.2%
↓-5.4%prior 149

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 with animals remained the leading contributing factor in both periods, with the count of such incidents increasing by 20% from 50 to 60. "Lost Control" was the second-most cited factor in both years, but its frequency decreased by 31.8%, from 22 crashes to 15. Incidents involving a failure to yield from a stop sign saw a significant reduction, falling 40% from 15 crashes in the prior year to 9 in the current year.

Officer-Reported Primary Contributing Cause

Animal60 (32.4%)↑20.0%prior 50
Lost Control15 (8.1%)↓-31.8%prior 22
Other (explain in narrative): Other13 (7%)↑44.4%prior 9
Driving too fast for conditions10 (5.4%)↑42.9%prior 7
Ran off road - straight9 (4.9%)→0.0%prior 9
FTYROW: From stop sign9 (4.9%)↓-40.0%prior 15
Followed too close8 (4.3%)↑60.0%prior 5
FTYROW: From yield sign8 (4.3%)↑33.3%prior 6
Ran off road - left7 (3.8%)→0.0%prior 7
Exceeded authorized speed5 (2.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

There was a notable shift in lighting conditions for crashes year-over-year. The proportion of crashes occurring in daylight fell from 57.7% to 48.1%, while the share of crashes on dark, unlit roadways rose from 14.9% to 22.7%. The distribution of crashes related to road surface conditions like dry, wet, or snow-covered roads remained relatively consistent between the two periods.

Weather

Clear108 (78.8%)
↓-14.3%prior 126
Cloudy14 (10.2%)
↓-6.7%prior 15
Snow6 (4.4%)
↑20.0%prior 5
Rain5 (3.6%)
Other (explain in narrative)3 (2.2%)
Freezing rain/drizzle1 (0.7%)

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

Lighting

Daylight89 (63.6%)
↓-23.3%prior 116
Dark - roadway not lighted42 (30.0%)
↑40.0%prior 30
Dawn4 (2.9%)
↓-33.3%prior 6
Dusk3 (2.1%)
↓-40.0%prior 5
Dark - roadway lighted2 (1.4%)
↓-77.8%prior 9

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

Road Surface

Dry94 (67.1%)
↓-19.7%prior 117
Gravel13 (9.3%)
↓-13.3%prior 15
Snow11 (7.9%)
↑37.5%prior 8
Ice/frost9 (6.4%)
↓-30.8%prior 13
Wet9 (6.4%)
→0.0%prior 9
Mud, dirt2 (1.4%)
Other (explain in narrative)2 (1.4%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes saw a shift in ranking, with Chevrolet (68 vehicles, combining "CHEV" and "CHEVROLET") surpassing Ford (51 vehicles). This represents a decrease for Ford, which was involved in 67 crashes in the prior year. Demographically, the 16-20 age group's share of persons involved in crashes increased from 13.2% to 16.0%, while the 65+ age group's representation decreased from 14.6% to 12.7%.

Top Vehicle Makes (256 vehicles)

1
CHEV55 (21.5%)
↑14.6%prior 48
2
FORD51 (19.9%)
↓-23.9%prior 67
3
CHEVROLET13 (5.1%)
↓-31.6%prior 19
4
NISS11 (4.3%)
↑10.0%prior 10
5
TOYT10 (3.9%)
↓-23.1%prior 13
6
DODG9 (3.5%)
↓-40.0%prior 15
7
JEEP8 (3.1%)
↓-11.1%prior 9
8
KIA7 (2.7%)
↓-12.5%prior 8
9
GMC7 (2.7%)
↓-12.5%prior 8
10
HOND6 (2.3%)
↓-33.3%prior 9

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

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

Sex Distribution (151 persons with recorded sex)

Male100 (66.2%)
↓-4.8%prior 105
Female51 (33.8%)
↓-30.1%prior 73

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: 185
  • Total persons involved: 268
  • Total vehicles involved: 256

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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