Monthly Traffic Safety Analysis

3,878 CRASHES IN
IOWA, IA
FEBRUARY 2022

All metrics benchmarked againstFebruary 2021

Total crashes across Iowa decreased by 24.9% in February 2022 compared to the same month in 2021, falling from 5,168 to 3,878. Despite the overall drop in collisions, the number of fatalities increased from 14 to 22. The most significant year-over-year change was a substantial reduction in crashes occurring on snow and ice-covered roads, suggesting a primary influence of improved weather conditions.

3,878

▼ -25.0%was 5,168

Total Crash Events

22

▲ 57.1%was 14

Persons Killed

1,141

▼ -12.4%was 1,303

Persons Injured

21

▲ 75.0%was 12

Fatal Crash Events

Note: "Persons Killed" (22) counts individual fatalities across all crash events. "Fatal" in the severity table below (21) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows a significant year-over-year decrease in total crashes, which fell by 1,290 incidents from 5,168 in February 2021 to 3,878 in February 2022. Total injuries also saw a decline from 1,303 to 1,141. However, this downward trend did not extend to fatalities, which increased from 14 to 22 over the same period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 1→0.0%

0

Cyclists Killed

Prior: 0→0.0%

21

Motorists Killed

Prior: 13↑61.5%

0

Other Killed

Prior: 0→0.0%

19

Pedestrians Injured

Prior: 17↑11.8%

5

Cyclists Injured

Prior: 2↑150.0%

1,111

Motorists Injured

Prior: 1,284↓-13.5%

6

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak hour for crashes remained consistent at 3 p.m. in both February 2022 (337 crashes) and February 2021 (388 crashes). However, the peak day of the week shifted from Thursday (952 crashes) in the prior year to Friday (762 crashes) in the current period. Crash volumes were lower on every day of the week in February 2022 compared to the previous year.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the severity of outcomes worsened. The number of fatal crashes increased from 12 to 21, and the total number of fatalities rose from 14 to 22. Consequently, the fatal crash rate more than doubled from 0.23% to 0.54% of all incidents. The proportion of crashes resulting in any injury also grew, rising from 23.0% in the prior period to 26.3% in the current period.

Severity is per crash event (most severe injury). 21 fatal crash events resulted in 22 persons killed.

Outcome by Severity (Crash Events)

Fatal21fatal crashes0.5%
↑75.0%prior 12
Serious Injury68serious injury crashes1.8%
↑28.3%prior 53
Minor Injury328minor injury crashes8.5%
↓-9.4%prior 362
Possible Injury620possible injury crashes16%
↓-20.1%prior 776
No Injury2,841no injury crashes73.3%
↓-28.3%prior 3,965

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors shifted significantly between the two periods, reflecting different driving conditions. In February 2021, "Driving too fast for conditions" was the top cause with 860 crashes; in February 2022, this factor's count dropped to 328. In the current period, collisions with animals became the leading factor, with the count increasing from 290 to 373. "Followed too close" remained a top-three factor in both years, with its count slightly increasing from 339 to 354.

Officer-Reported Primary Contributing Cause

Animal373 (9.6%)↑28.6%prior 290
Followed too close354 (9.1%)↑4.4%prior 339
Driving too fast for conditions328 (8.5%)↓-61.9%prior 860
Ran off road - left294 (7.6%)↓-51.2%prior 602
Other (explain in narrative): Other254 (6.5%)↓-10.6%prior 284
Lost Control232 (6%)↓-22.4%prior 299
FTYROW: From stop sign197 (5.1%)↓-23.3%prior 257
FTYROW: Making left turn173 (4.5%)↓-11.7%prior 196
Ran off road - straight151 (3.9%)↓-31.1%prior 219
Ran Traffic Signal131 (3.4%)↓-16.6%prior 157

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

A dramatic year-over-year shift occurred in crash conditions, primarily related to weather. Crashes on snow-covered roads decreased from 1,526 to 437, and incidents on icy roads fell from 1,225 to 392. In contrast, crashes on dry roads increased from 1,558 to 2,416. This data strongly suggests that February 2021 had more severe winter weather, which was a key driver of its higher overall crash volume.

Weather

Clear2,364 (66.7%)
↓-12.8%prior 2,710
Cloudy586 (16.5%)
↓-38.8%prior 958
Snow341 (9.6%)
↓-60.9%prior 872
Freezing rain/drizzle155 (4.4%)
↑287.5%prior 40
Blowing Snow49 (1.4%)
↓-79.6%prior 240
Rain25 (0.7%)
Other (explain in narrative)10 (0.3%)
↓-41.2%prior 17
Severe Winds10 (0.3%)
↓-54.5%prior 22
Sleet, hail5 (0.1%)
↓-66.7%prior 15

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · Weather condition at time of crash

Lighting

Daylight2,238 (63.0%)
↓-33.7%prior 3,375
Dark - roadway lighted677 (19.1%)
↓-15.1%prior 797
Dark - roadway not lighted427 (12.0%)
↓-11.6%prior 483
Dusk115 (3.2%)
↓-15.4%prior 136
Dawn74 (2.1%)
↓-32.1%prior 109
Dark - unknown roadway lighting20 (0.6%)
↓-16.7%prior 24

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · Lighting condition field

Road Surface

Dry2,416 (67.9%)
↑55.1%prior 1,558
Snow437 (12.3%)
↓-71.4%prior 1,526
Ice/frost392 (11.0%)
↓-68.0%prior 1,225
Wet214 (6.0%)
↓-46.1%prior 397
Gravel45 (1.3%)
↑221.4%prior 14
Slush41 (1.2%)
↓-77.5%prior 182
Mud, dirt7 (0.2%)
Other (explain in narrative)4 (0.1%)
↓-75.0%prior 16
Sand1 (0.0%)
↓-88.9%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · Road surface condition field

Vehicles & Demographics

The distribution of vehicle makes involved in crashes remained stable year-over-year, with Ford and Chevrolet models consistently representing the largest number of vehicles in both periods. The demographic profile of persons involved in crashes also showed little change. The 26-34 age group constituted the largest single cohort of individuals involved in collisions in both February 2022 and February 2021.

Top Vehicle Makes (6,670 vehicles)

1
FORD1,080 (16.2%)
↓-26.6%prior 1,471
2
CHEV856 (12.8%)
↓-9.7%prior 948
3
CHEVROLET430 (6.4%)
↓-46.4%prior 802
4
TOYT310 (4.6%)
↓-4.9%prior 326
5
JEEP252 (3.8%)
↓-31.7%prior 369
6
GMC248 (3.7%)
↓-18.7%prior 305
7
HOND224 (3.4%)
↑13.1%prior 198
8
NR223 (3.3%)
↓-7.9%prior 242
9
DODG217 (3.3%)
↓-27.2%prior 298
10
KIA188 (2.8%)
↓-23.9%prior 247

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · Vehicle unit records

1,255 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (5,935 persons with recorded sex)

Male3,404 (57.4%)
↓-29.5%prior 4,825
Female2,531 (42.6%)
↓-22.7%prior 3,275

Source: Iowa Crash Data · ArcGIS Open Data · 2022-02-01 to 2022-02-28 · 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: 2022-02-01 through 2022-02-28
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2022-02-01 through 2022-02-28 (28 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 3,878
  • Total persons involved: 8,783
  • Total vehicles involved: 6,670

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: February 2022." Published September 9, 2026. Reporting period: 2022-02-01 to 2022-02-28. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/february-2022-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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