Yearly Traffic Safety Analysis

635 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Marshall County, total traffic crashes decreased by 6.9% from 682 in 2024 to 635 in 2025. Despite this overall reduction in collisions, the number of fatalities resulting from these crashes increased from 4 to 7. The most notable year-over-year shift was this increase in crash severity, with the number of fatal crashes rising from 4 to 7, even as the total number of crashes declined.

635

▼ -6.9%was 682

Total Crash Events

7

▲ 75.0%was 4

Persons Killed

193

▲ 4.3%was 185

Persons Injured

7

▲ 75.0%was 4

Fatal Crash Events

Note: "Persons Killed" (7) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) 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 volume in Marshall County saw a downward trend, decreasing by 6.9% from 682 incidents in the prior year to 635 in the current year. However, this decrease in total crashes did not correspond to a decrease in severity. Total injuries increased slightly from 185 to 193, and total fatalities rose from 4 to 7, indicating that while fewer crashes occurred, they were more severe on average.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 4↑50.0%

2

Pedestrians Injured

Prior: 1↑100.0%

2

Cyclists Injured

Prior: 4↓-50.0%

189

Motorists Injured

Prior: 180↑5.0%

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 temporal patterns of crashes showed a shift between the two periods. The peak day for crashes moved from Tuesday (114 crashes) in the prior year to Thursday (113 crashes) in the current year. Similarly, the peak hour for collisions shifted two hours earlier, from 5 p.m. (48 crashes) in the prior period to 3 p.m. (58 crashes) in the current period.

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 worsened year-over-year despite a drop in total incidents. The number of fatal crashes increased from 4 to 7, causing the fatal crash rate to rise from 0.6% to 1.1% of all crashes. While the proportion of serious injury crashes remained stable (3.4% vs. 3.5%), the share of minor injury crashes increased from 9.1% to 10.6%. Conversely, crashes resulting in possible injury or no injury saw a decrease in their respective proportions.

Outcome by Severity (Crash Events)

Fatal7fatal crashes1.1%
↑75.0%prior 4
Serious Injury22serious injury crashes3.5%
↓-4.3%prior 23
Minor Injury67minor injury crashes10.6%
↑8.1%prior 62
Possible Injury74possible injury crashes11.7%
↓-17.8%prior 90
No Injury465no injury crashes73.2%
↓-7.6%prior 503

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 factors for crashes remained consistent, though their counts changed. Collisions involving an animal remained the top factor in both periods but decreased in count from 147 to 117. The second-most common factor, failure to yield right-of-way from a stop sign, also saw a slight decrease from 63 to 60 incidents. Notably, crashes attributed to 'Driving too fast for conditions' increased by 29.6% in count, rising from 27 to 35 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal117 (18.4%)↓-20.4%prior 147
FTYROW: From stop sign60 (9.4%)↓-4.8%prior 63
Lost Control40 (6.3%)↑11.1%prior 36
FTYROW: Making left turn35 (5.5%)↑29.6%prior 27
Driving too fast for conditions35 (5.5%)↑29.6%prior 27
Ran Stop Sign29 (4.6%)↑16.0%prior 25
Other (explain in narrative): Other28 (4.4%)↓-12.5%prior 32
Followed too close27 (4.3%)↓-22.9%prior 35
Driver Distraction: Other interior distraction25 (3.9%)↓-28.6%prior 35
Ran off road - left25 (3.9%)↑8.7%prior 23

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 different environmental conditions remained largely stable year-over-year. In both periods, approximately 60% of crashes occurred in clear weather and 63% on dry road surfaces. There was a minor shift in crashes on adverse road surfaces, with the count of crashes on snow increasing from 33 to 39 and on ice/frost from 18 to 25. Lighting conditions for crashes also showed little proportional change, with daylight crashes accounting for 55% of incidents in the current period compared to 53.5% in the prior period.

Weather

Clear382 (72.1%)
↓-6.1%prior 407
Cloudy87 (16.4%)
↑3.6%prior 84
Rain24 (4.5%)
↓-20.0%prior 30
Snow20 (3.8%)
↑25.0%prior 16
Fog, smoke, smog5 (0.9%)
↓-44.4%prior 9
Freezing rain/drizzle5 (0.9%)
→0.0%prior 5
Severe Winds4 (0.8%)
Blowing Snow3 (0.6%)
↓-40.0%prior 5

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

Lighting

Daylight349 (65.1%)
↓-4.4%prior 365
Dark - roadway not lighted81 (15.1%)
↓-5.8%prior 86
Dark - roadway lighted73 (13.6%)
↑1.4%prior 72
Dawn17 (3.2%)
→0.0%prior 17
Dusk12 (2.2%)
↓-42.9%prior 21
Dark - unknown roadway lighting4 (0.7%)
↓-50.0%prior 8

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

Road Surface

Dry405 (76.1%)
↓-5.4%prior 428
Wet41 (7.7%)
↓-34.9%prior 63
Snow39 (7.3%)
↑18.2%prior 33
Ice/frost25 (4.7%)
↑38.9%prior 18
Gravel15 (2.8%)
↑15.4%prior 13
Slush7 (1.3%)
→0.0%prior 7

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

Vehicles & Demographics

The makes of vehicles most frequently involved in crashes remained consistent, with Ford (155 vehicles) and Chevrolet (153 vehicles) leading in the current period. This represents a slight shift from the prior year when Chevrolet (175) was more prevalent than Ford (166). The age distribution of persons involved in crashes also remained stable, with all primary adult age brackets (16 through 65+) showing similar representation year-over-year, though absolute counts decreased in line with the lower number of total persons involved.

Top Vehicle Makes (1,020 vehicles)

1
FORD155 (15.2%)
↓-6.6%prior 166
2
CHEV153 (15%)
↓-12.6%prior 175
3
HOND61 (6%)
↑8.9%prior 56
4
DODG52 (5.1%)
↓-16.1%prior 62
5
JEEP51 (5%)
↓-3.8%prior 53
6
GMC44 (4.3%)
↓-6.4%prior 47
7
TOYT43 (4.2%)
↓-17.3%prior 52
8
TOYO37 (3.6%)
↑37.0%prior 27
9
CHEVROLET35 (3.4%)
↓-36.4%prior 55
10
NISS33 (3.2%)
↓-26.7%prior 45

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

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

Sex Distribution (622 persons with recorded sex)

Male358 (57.6%)
↓-11.8%prior 406
Female264 (42.4%)
↓-1.9%prior 269

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: 635
  • Total persons involved: 1,064
  • Total vehicles involved: 1,020

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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