Yearly Traffic Safety Analysis

597 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Muscatine County, total traffic crashes increased by 12% from 533 in 2024 to 597 in 2025. Despite the overall rise in collisions, there was a notable year-over-year decrease in crash severity, with total fatalities dropping from 9 to 4 and DUI-involved crashes decreasing from 28 to 11.

597

▲ 12.0%was 533

Total Crash Events

4

▼ -55.6%was 9

Persons Killed

194

▲ 0.5%was 193

Persons Injured

4

▼ -55.6%was 9

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 crash trends in Muscatine County show an increase in volume year-over-year. Total crashes rose from 533 in 2024 to 597 in 2025, a 12% increase. However, this rise in incidents did not correspond with an increase in severity, as total fatalities fell from 9 to 4, while the number of injuries remained stable at 194, compared to 193 in the prior year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 9↓-66.7%

3

Pedestrians Injured

Prior: 4↓-25.0%

5

Cyclists Injured

Prior: 3↑66.7%

186

Motorists Injured

Prior: 186→0.0%

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 shifted between the two periods. In 2025, the peak day for crashes was Thursday with 108 incidents, a change from Friday (98 incidents) in the previous year. The peak hour also shifted from the evening commute to the morning, with 7 a.m. (43 crashes) becoming the most frequent crash time in 2025, compared to 5 p.m. (40 crashes) in 2024.

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 in 2025 compared to the prior year. The number of fatal crashes fell from 9 to 4, and the fatal crash rate dropped from 1.69 per 100 crashes to 0.67. The proportion of crashes resulting in serious injuries also declined from 3.4% to 2.3% of all incidents. Overall, crashes involving any level of injury (possible, minor, or serious) represented a smaller share of the total, decreasing from 27.2% in 2024 to 23.6% in 2025.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.7%
↓-55.6%prior 9
Serious Injury14serious injury crashes2.3%
↓-22.2%prior 18
Minor Injury52minor injury crashes8.7%
↓-5.5%prior 55
Possible Injury75possible injury crashes12.6%
↑4.2%prior 72
No Injury452no injury crashes75.7%
↑19.3%prior 379

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

The leading contributing factor in both years was 'Animal,' with its count increasing by 28.5% from 158 incidents in 2024 to 203 in 2025. 'Followed too close' and 'FTYROW: From stop sign' remained the second and third most common factors, with their counts remaining relatively stable. A notable shift was observed in crashes attributed to 'FTYROW: Making left turn,' which increased in count by 61.9% from 21 to 34, while crashes involving 'Driving too fast for conditions' saw a 60.7% decrease in count, from 28 to 11.

Officer-Reported Primary Contributing Cause

Animal203 (34%)↑28.5%prior 158
Followed too close36 (6%)↓-5.3%prior 38
FTYROW: From stop sign36 (6%)→0.0%prior 36
FTYROW: Making left turn34 (5.7%)↑61.9%prior 21
Other (explain in narrative): Other29 (4.9%)↑11.5%prior 26
Ran off road - left27 (4.5%)↑3.8%prior 26
Ran Stop Sign22 (3.7%)↓-12.0%prior 25
Lost Control22 (3.7%)↓-15.4%prior 26
Ran Traffic Signal18 (3%)↑125.0%prior 8
Ran off road - straight13 (2.2%)

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

Road & Environmental Conditions

Crash conditions remained broadly consistent year-over-year, with the majority of incidents in both periods occurring in clear weather during daylight hours on dry roads. In 2025, 52.6% of crashes occurred in clear weather and 56.8% on dry roads, compared to 55.3% and 58.2% respectively in 2024. The proportion of crashes occurring in daylight was nearly unchanged at approximately 49% for both years. There was a slight decrease in the number of crashes on snow-covered roads, from 24 in 2024 to 19 in 2025.

Weather

Clear314 (77.0%)
↑6.4%prior 295
Cloudy57 (14.0%)
↑11.8%prior 51
Snow14 (3.4%)
↓-36.4%prior 22
Rain12 (2.9%)
↓-14.3%prior 14
Fog, smoke, smog5 (1.2%)
Blowing Snow3 (0.7%)
Freezing rain/drizzle2 (0.5%)
↓-71.4%prior 7
Sleet, hail1 (0.2%)

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

Lighting

Daylight295 (71.6%)
↑13.0%prior 261
Dark - roadway lighted53 (12.9%)
↓-5.4%prior 56
Dark - roadway not lighted40 (9.7%)
↓-29.8%prior 57
Dawn11 (2.7%)
↑10.0%prior 10
Dusk8 (1.9%)
↓-42.9%prior 14
Dark - unknown roadway lighting5 (1.2%)

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

Road Surface

Dry339 (83.1%)
↑9.4%prior 310
Wet26 (6.4%)
↓-25.7%prior 35
Snow19 (4.7%)
↓-20.8%prior 24
Ice/frost14 (3.4%)
↓-22.2%prior 18
Gravel7 (1.7%)
↓-30.0%prior 10
Slush2 (0.5%)
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

The top three vehicle makes involved in crashes—Ford, Chevrolet, and Toyota—remained the same in both 2024 and 2025, with all three seeing an increase in their total counts. An analysis of persons involved in crashes shows a notable increase in several age demographics. The number of persons aged 21-25 involved in crashes grew from 74 to 102, and the 65+ age group increased from 104 to 129 persons year-over-year.

Top Vehicle Makes (922 vehicles)

1
FORD165 (17.9%)
↑19.6%prior 138
2
CHEV127 (13.8%)
↑16.5%prior 109
3
TOYT86 (9.3%)
↑62.3%prior 53
4
DODG40 (4.3%)
↑11.1%prior 36
5
KIA39 (4.2%)
↑39.3%prior 28
6
GMC37 (4%)
↓-14.0%prior 43
7
CHEVROLET37 (4%)
↓-17.8%prior 45
8
NISS33 (3.6%)
↑10.0%prior 30
9
HOND32 (3.5%)
↓-15.8%prior 38
10
TOYOTA27 (2.9%)
↑58.8%prior 17

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

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

Sex Distribution (494 persons with recorded sex)

Male283 (57.3%)
↑8.4%prior 261
Female211 (42.7%)
↑15.3%prior 183

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: 597
  • Total persons involved: 965
  • Total vehicles involved: 922

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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