Monthly Traffic Safety Analysis

3,846 CRASHES IN
IOWA, IA
MARCH 2025

All metrics benchmarked againstMarch 2024

In March 2025, Iowa recorded 3,846 motor vehicle crashes, a 3.0% increase from the 3,733 crashes in March 2024. Despite the rise in total incidents, the number of fatalities decreased significantly, dropping 34.6% from 26 to 17 year-over-year. The most notable shift in crash characteristics was a 42% increase in the count of single-vehicle, non-collision incidents, which rose from 1,008 to 1,431.

3,846

▲ 3.0%was 3,733

Total Crash Events

17

▼ -34.6%was 26

Persons Killed

1,266

▲ 11.6%was 1,134

Persons Injured

17

▼ -32.0%was 25

Fatal Crash Events

Note: "Persons Killed" (17) counts individual fatalities across all crash events. "Fatal" in the severity table below (17) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 1 crash with unreported severity is not shown in the severity breakdown.

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends for March show a slight increase in volume compared to the previous year, with total crashes rising by 3.0% from 3,733 to 3,846. While total injuries also saw an 11.6% increase from 1,134 to 1,266, there was a positive counter-trend in fatalities, which declined by 34.6% from 26 to 17.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 4↓-75.0%

0

Cyclists Killed

Prior: 0→0.0%

16

Motorists Killed

Prior: 22↓-27.3%

0

Other Killed

Prior: 0→0.0%

28

Pedestrians Injured

Prior: 26↑7.7%

17

Cyclists Injured

Prior: 15↑13.3%

1,219

Motorists Injured

Prior: 1,091↑11.7%

2

Other Injured

Prior: 2→0.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · 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 significantly year-over-year. The peak day for crashes moved from Friday (917 incidents) in March 2024 to Wednesday (662 incidents) in March 2025, with Friday crashes dropping substantially. The peak hour for crashes remained consistent within the afternoon commute, shifting slightly from the 3 p.m. hour (300 crashes) in the prior year to the 4 p.m. hour (312 crashes) in the current period.

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity outcomes improved compared to the prior year, with the fatal crash rate decreasing from 0.7% to 0.4% of all incidents. Fatal crashes fell from 25 to 17. Conversely, the number of crashes involving serious injuries increased from 81 to 94, and the total count of crashes with any reported injury (possible, minor, or serious) grew from 1,040 to 1,073.

Outcome by Severity (Crash Events)

Fatal17fatal crashes0.4%
↓-32.0%prior 25
Serious Injury94serious injury crashes2.4%
↑16.0%prior 81
Minor Injury353minor injury crashes9.2%
↓-2.2%prior 361
Possible Injury626possible injury crashes16.3%
↑10.2%prior 568
No Injury2,755no injury crashes71.6%
↑2.1%prior 2,698

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · Most severe injury per crash record

Top Contributing Factors

The primary contributing factors remained largely consistent in ranking, though their counts shifted. Collisions with animals were the leading factor in both periods, with the count increasing by 22.4% from 441 to 540 crashes. Following too closely remained the second-most cited factor, its count growing by 8.8% from 317 to 345. Meanwhile, crashes attributed to running off the road to the left decreased by 7.9% from 253 to 233 incidents.

Officer-Reported Primary Contributing Cause

Animal540 (14%)↑22.4%prior 441
Followed too close345 (9%)↑8.8%prior 317
Ran off road - left233 (6.1%)↓-7.9%prior 253
Driving too fast for conditions223 (5.8%)↑11.5%prior 200
FTYROW: From stop sign203 (5.3%)↑9.1%prior 186
Lost Control198 (5.1%)↑5.3%prior 188
Other (explain in narrative): Other187 (4.9%)↓-8.8%prior 205
FTYROW: Making left turn163 (4.2%)↓-3.0%prior 168
Ran Traffic Signal156 (4.1%)↑2.6%prior 152
Ran off road - straight145 (3.8%)↓-8.2%prior 158

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes by lighting conditions remained stable, with daylight crashes accounting for approximately 64% in both periods. However, there was a shift in road surface conditions, with the number of crashes on snow, ice, or slush-covered roads increasing from 349 to 442 year-over-year. This corresponds to an increase in the proportion of total crashes occurring on these surfaces from 9.4% in March 2024 to 11.5% in March 2025.

Weather

Clear2,388 (71.3%)
↑7.2%prior 2,228
Cloudy395 (11.8%)
↓-33.8%prior 597
Rain149 (4.4%)
↓-22.4%prior 192
Blowing Snow126 (3.8%)
↑800.0%prior 14
Snow122 (3.6%)
↓-22.3%prior 157
Severe Winds75 (2.2%)
↑971.4%prior 7
Freezing rain/drizzle57 (1.7%)
↓-40.0%prior 95
Sleet, hail29 (0.9%)
↓-12.1%prior 33
Other (explain in narrative)5 (0.1%)
↓-44.4%prior 9
Fog, smoke, smog4 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · Weather condition at time of crash

Lighting

Daylight2,452 (72.2%)
↑3.5%prior 2,369
Dark - roadway lighted440 (13.0%)
↓-4.1%prior 459
Dark - roadway not lighted313 (9.2%)
↓-11.1%prior 352
Dawn80 (2.4%)
↑1.3%prior 79
Dusk74 (2.2%)
↑13.8%prior 65
Dark - unknown roadway lighting35 (1.0%)
↑25.0%prior 28

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · Lighting condition field

Road Surface

Dry2,560 (76.2%)
↓-0.6%prior 2,575
Wet302 (9.0%)
↓-18.6%prior 371
Snow220 (6.5%)
↑59.4%prior 138
Ice/frost142 (4.2%)
↑32.7%prior 107
Slush80 (2.4%)
↓-23.1%prior 104
Gravel44 (1.3%)
↓-4.3%prior 46
Mud, dirt6 (0.2%)
↑20.0%prior 5
Other (explain in narrative)3 (0.1%)
Sand2 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes showed little change, with Ford and Chevrolet models consistently ranking as the top two in both March 2024 and March 2025. Analysis of the age of persons involved in crashes reveals a demographic shift. The proportion of individuals in the 16-20 age group decreased from 13.3% to 11.8% of all persons involved, while the 26-34 age group's representation increased from 14.8% to 16.4%.

Top Vehicle Makes (6,530 vehicles)

1
FORD980 (15%)
↓-0.8%prior 988
2
CHEV899 (13.8%)
↑1.9%prior 882
3
JEEP316 (4.8%)
↑3.9%prior 304
4
TOYT294 (4.5%)
↓-6.4%prior 314
5
CHEVROLET294 (4.5%)
↓-12.5%prior 336
6
HOND261 (4%)
↓-5.4%prior 276
7
GMC220 (3.4%)
↓-2.2%prior 225
8
DODG215 (3.3%)
↓-14.3%prior 251
9
KIA201 (3.1%)
↑14.2%prior 176
10
NISS187 (2.9%)
↓-1.1%prior 189

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · Vehicle unit records

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

Sex Distribution (4,186 persons with recorded sex)

Male2,470 (59.0%)
↑1.6%prior 2,431
Female1,716 (41.0%)
↓-4.0%prior 1,788

Source: Iowa Crash Data · ArcGIS Open Data · 2025-03-01 to 2025-03-31 · 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: 2025-03-01 through 2025-03-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2025-03-01 through 2025-03-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 3,846
  • Total persons involved: 6,839
  • Total vehicles involved: 6,530

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: March 2025." Published September 9, 2026. Reporting period: 2025-03-01 to 2025-03-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/march-2025-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

ThatCarHitMe.com · An Injuria.ai Company