Monthly Traffic Safety Analysis

3,867 CRASHES IN
IOWA, IA
MARCH 2026

All metrics benchmarked againstMarch 2025

In March 2026, there were 3,867 total crashes, a slight increase of 0.5% from the 3,846 crashes recorded in March 2025. While the overall crash volume remained stable, the number of fatalities rose significantly, increasing by 82.4% from 17 in the prior year to 31 in the current period. Total injuries, however, saw a minor decrease of 1.7% from 1,266 to 1,245.

3,867

▲ 0.5%was 3,846

Total Crash Events

31

▲ 82.4%was 17

Persons Killed

1,245

▼ -1.7%was 1,266

Persons Injured

27

▲ 58.8%was 17

Fatal Crash Events

Note: "Persons Killed" (31) counts individual fatalities across all crash events. "Fatal" in the severity table below (27) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume remained relatively stable year-over-year, with a minor 0.5% increase from 3,846 crashes in March 2025 to 3,867 in March 2026. The most significant trend was the increase in crash severity, as total fatalities rose from 17 to 31, an 82.4% increase. In contrast, total reported injuries decreased slightly from 1,266 to 1,245.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 1→0.0%

1

Cyclists Killed

Prior: 0→%

29

Motorists Killed

Prior: 16↑81.3%

0

Other Killed

Prior: 0→0.0%

23

Pedestrians Injured

Prior: 28↓-17.9%

22

Cyclists Injured

Prior: 17↑29.4%

1,194

Motorists Injured

Prior: 1,219↓-2.1%

6

Other Injured

Prior: 2↑200.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed a shift between the two periods. In March 2026, the peak day for crashes was Monday with 715 incidents, a change from March 2025 when Wednesday was the peak day with 662 crashes. The peak hour for collisions also shifted slightly earlier, moving from the 4 p.m. hour in the prior year (312 crashes) to the 3 p.m. hour in the current period (312 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes worsened year-over-year, with the number of fatal crashes increasing from 17 to 27, which raised the fatal crash rate from 0.4% to 0.7% of all incidents. The share of serious injury crashes decreased from 2.4% to 1.8% of all collisions. Meanwhile, the proportion of minor injury crashes increased slightly from 9.2% to 9.6% of the total.

Severity is per crash event (most severe injury). 27 fatal crash events resulted in 31 persons killed.

Outcome by Severity (Crash Events)

Fatal27fatal crashes0.7%
↑58.8%prior 17
Serious Injury71serious injury crashes1.8%
↓-24.5%prior 94
Minor Injury373minor injury crashes9.6%
↑5.7%prior 353
Possible Injury602possible injury crashes15.6%
↓-3.8%prior 626
No Injury2,794no injury crashes72.3%
↑1.4%prior 2,755

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes remained consistent year-over-year, though their counts shifted. Collisions involving an animal continued to be the top factor in March 2026 with 451 crashes, but this represents a 16.5% decrease in count from 540 in the prior year. Conversely, crashes attributed to 'Followed too close' increased in count by 15.9% from 345 to 400, becoming the second most common factor. The third-ranked factor, 'Ran off road - left,' saw a small increase in count from 233 to 241.

Officer-Reported Primary Contributing Cause

Animal451 (11.7%)↓-16.5%prior 540
Followed too close400 (10.3%)↑15.9%prior 345
Ran off road - left241 (6.2%)↑3.4%prior 233
Other (explain in narrative): Other210 (5.4%)↑12.3%prior 187
Driving too fast for conditions207 (5.4%)↓-7.2%prior 223
Lost Control198 (5.1%)→0.0%prior 198
FTYROW: From stop sign189 (4.9%)↓-6.9%prior 203
FTYROW: Making left turn165 (4.3%)↑1.2%prior 163
Ran Traffic Signal155 (4%)↓-0.6%prior 156
Driver Distraction: Other interior distraction147 (3.8%)↑12.2%prior 131

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in clear weather and daylight conditions remained the dominant categories, with counts staying relatively stable year-over-year. There was a notable increase in crashes occurring on roads with ice or frost, which rose from 142 incidents in March 2025 to 205 in March 2026. Conversely, crashes on wet surfaces decreased from 302 to 271, and those on snowy surfaces fell from 220 to 189.

Weather

Clear2,418 (70.2%)
↑1.3%prior 2,388
Cloudy601 (17.5%)
↑52.2%prior 395
Blowing Snow127 (3.7%)
↑0.8%prior 126
Snow108 (3.1%)
↓-11.5%prior 122
Rain70 (2.0%)
↓-53.0%prior 149
Severe Winds51 (1.5%)
↓-32.0%prior 75
Freezing rain/drizzle45 (1.3%)
↓-21.1%prior 57
Fog, smoke, smog16 (0.5%)
Other (explain in narrative)5 (0.1%)
→0.0%prior 5
Sleet, hail3 (0.1%)
↓-89.7%prior 29

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · Weather condition at time of crash

Lighting

Daylight2,465 (71.3%)
↑0.5%prior 2,452
Dark - roadway lighted471 (13.6%)
↑7.0%prior 440
Dark - roadway not lighted350 (10.1%)
↑11.8%prior 313
Dawn91 (2.6%)
↑13.8%prior 80
Dusk67 (1.9%)
↓-9.5%prior 74
Dark - unknown roadway lighting14 (0.4%)
↓-60.0%prior 35

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · Lighting condition field

Road Surface

Dry2,704 (78.2%)
↑5.6%prior 2,560
Wet271 (7.8%)
↓-10.3%prior 302
Ice/frost205 (5.9%)
↑44.4%prior 142
Snow189 (5.5%)
↓-14.1%prior 220
Gravel49 (1.4%)
↑11.4%prior 44
Slush31 (0.9%)
↓-61.3%prior 80
Other (explain in narrative)4 (0.1%)
Mud, dirt3 (0.1%)
↓-50.0%prior 6
Sand1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford (1,068 vehicles) and Chevrolet-branded vehicles (1,191 combined) leading in both periods. The total number of vehicles involved in crashes saw a small increase from 6,530 to 6,632. An analysis of persons involved shows a shift in age demographics, with an increase in individuals aged 16-20 (from 807 to 872) and a decrease in the 26-34 age group (from 1,119 to 1,043).

Top Vehicle Makes (6,632 vehicles)

1
FORD1,068 (16.1%)
↑9.0%prior 980
2
CHEV926 (14%)
↑3.0%prior 899
3
TOYT332 (5%)
↑12.9%prior 294
4
JEEP328 (4.9%)
↑3.8%prior 316
5
HOND291 (4.4%)
↑11.5%prior 261
6
CHEVROLET265 (4%)
↓-9.9%prior 294
7
NISS246 (3.7%)
↑31.6%prior 187
8
GMC236 (3.6%)
↑7.3%prior 220
9
DODG217 (3.3%)
↑0.9%prior 215
10
KIA194 (2.9%)
↓-3.5%prior 201

Source: Iowa Crash Data · ArcGIS Open Data · 2026-03-01 to 2026-03-31 · Vehicle unit records

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

Sex Distribution (4,282 persons with recorded sex)

Male2,515 (58.7%)
↑1.8%prior 2,470
Female1,767 (41.3%)
↑3.0%prior 1,716

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

Data Coverage

  • Reporting period: 2026-03-01 through 2026-03-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 3,867
  • Total persons involved: 6,897
  • Total vehicles involved: 6,632

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