Yearly Traffic Safety Analysis

73 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Monroe County recorded 73 total traffic crashes, a 34.2% decrease from the 111 crashes reported in 2024. While total injuries remained relatively stable, increasing from 22 to 24, the number of fatalities dropped from one in the prior year to zero in the current year. A significant factor in the overall crash reduction was a sharp decline in collisions involving animals, which fell from 40 incidents in 2024 to 14 in 2025.

73

▼ -34.2%was 111

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

24

▲ 9.1%was 22

Persons Injured

0

▼ -100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

Traffic crashes in Monroe County showed a significant downward trend year-over-year, falling from 111 incidents in 2024 to 73 in 2025, a decrease of 34.2%. Despite the overall reduction in collisions, the number of people injured saw a slight increase from 22 to 24. However, fatal crashes were eliminated, with zero recorded in 2025 compared to one fatal crash resulting in one death the previous year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 0→%

23

Motorists Injured

Prior: 22↑4.5%

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 between the two periods. While Friday remained the peak day for crashes in both 2025 (18 crashes) and 2024 (23 crashes), the peak hour for incidents moved from the morning to the afternoon. In 2024, the highest frequency of crashes occurred at 7 AM with 11 incidents. In 2025, the peak shifted to the afternoon, with the hours of 2 PM and 4 PM each recording 8 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

Crash severity improved year-over-year, with fatal crashes decreasing from one in 2024 to zero in 2025. The number of serious injury crashes also fell from two to one. However, the proportion of crashes resulting in a possible injury increased significantly, rising from 4.5% of all crashes in 2024 (5 incidents) to 20.5% in 2025 (15 incidents). Crashes resulting in no injury represented 74% of the total in 2025, down from 84.7% in the prior year.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.4%
↓-50.0%prior 2
Minor Injury3minor injury crashes4.1%
↓-66.7%prior 9
Possible Injury15possible injury crashes20.5%
↑200.0%prior 5
No Injury54no injury crashes74%
↓-42.6%prior 94

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, but the count dropped by 65%, from 40 incidents in 2024 to 14 in 2025. Consequently, its share of all crashes decreased from 36% to 19.2%. Crashes attributed to 'Followed too close' also saw a significant reduction, falling from 8 incidents to 3. Conversely, crashes attributed to 'Lost Control' increased from 2 in 2024 to 5 in 2025, and 'Driving too fast for conditions' rose slightly from 6 to 7 incidents.

Officer-Reported Primary Contributing Cause

Animal14 (19.2%)↓-65.0%prior 40
Driving too fast for conditions7 (9.6%)↑16.7%prior 6
Lost Control5 (6.8%)
Ran off road - left4 (5.5%)↓-20.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner4 (5.5%)
Followed too close3 (4.1%)↓-62.5%prior 8
Other (explain in narrative): No improper action3 (4.1%)
Swerving/Evasive Action3 (4.1%)
Failed to keep in proper lane2 (2.7%)↓-60.0%prior 5
Ran Stop Sign2 (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

The majority of crashes in both years occurred in clear weather on dry roads. In 2025, 60.3% of crashes happened in clear weather, up from 55% in 2024. Crashes on dry road surfaces accounted for 61.6% of the total in 2025, an increase from 51.4% in the prior year. The proportion of incidents occurring during daylight hours also rose, from 49.5% in 2024 to 57.5% in 2025.

Weather

Clear44 (73.3%)
↓-27.9%prior 61
Cloudy8 (13.3%)
↓-20.0%prior 10
Rain3 (5.0%)
Snow3 (5.0%)
↓-40.0%prior 5
Blowing Snow2 (3.3%)

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

Lighting

Daylight42 (70.0%)
↓-23.6%prior 55
Dark - roadway not lighted12 (20.0%)
↑20.0%prior 10
Dark - roadway lighted4 (6.7%)
↓-20.0%prior 5
Dusk2 (3.3%)

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

Road Surface

Dry45 (75.0%)
↓-21.1%prior 57
Snow7 (11.7%)
↓-12.5%prior 8
Wet6 (10.0%)
Gravel2 (3.3%)
↓-71.4%prior 7

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

Vehicles & Demographics

Ford and Chevrolet remained the top two vehicle makes involved in crashes for both years, though their counts decreased in line with the overall trend, from 38 to 23 for Ford and 35 to 23 for Chevrolet. The number of Dodge-family vehicles (including Ram) involved remained relatively stable, with 16 in 2025 compared to 17 in 2024. Analysis of persons involved shows a shift in age demographics; in 2025, the 45-54 age group was most represented with 18 individuals, followed by the 16-20 age group with 17. This contrasts with 2024, when the 35-44, 26-34, and 55-64 age groups were the most frequently involved.

Top Vehicle Makes (111 vehicles)

1
FORD23 (20.7%)
↓-39.5%prior 38
2
CHEV18 (16.2%)
↓-33.3%prior 27
3
DODG7 (6.3%)
↑16.7%prior 6
4
CHEVROLET5 (4.5%)
↓-37.5%prior 8
5
DODGE5 (4.5%)
6
TOYT5 (4.5%)
7
GMC4 (3.6%)
↓-55.6%prior 9
8
JEEP4 (3.6%)
↓-50.0%prior 8
9
RAM4 (3.6%)
↓-42.9%prior 7
10
HOND3 (2.7%)

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

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

Sex Distribution (62 persons with recorded sex)

Male42 (67.7%)
↓-32.3%prior 62
Female20 (32.3%)
↓-50.0%prior 40

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: 73
  • Total persons involved: 118
  • Total vehicles involved: 111

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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