Monthly Traffic Safety Analysis

3,598 CRASHES IN
IOWA, IA
MARCH 2022

All metrics benchmarked againstMarch 2021

In March 2022, there were 3,598 total crashes recorded in Iowa, representing a 2.7% increase from the 3,505 crashes documented in March 2021. The most notable year-over-year change was a 55.6% increase in total fatalities, which rose from 18 to 28. Total injuries also increased by 6.1%, from 1,067 to 1,132, reflecting a general upward trend in crash severity and volume.

3,598

2.7%was 3,505

Total Crash Events

28

55.6%was 18

Persons Killed

1,132

6.1%was 1,067

Persons Injured

23

35.3%was 17

Fatal Crash Events

Note: "Persons Killed" (28) counts individual fatalities across all crash events. "Fatal" in the severity table below (23) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends show a slight increase year-over-year. Total crashes rose by 2.7%, from 3,505 in March 2021 to 3,598 in March 2022. This was accompanied by a more substantial 55.6% increase in fatalities (from 18 to 28) and a 6.1% increase in injuries (from 1,067 to 1,132).

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 20.0%

0

Cyclists Killed

Prior: 00.0%

26

Motorists Killed

Prior: 1662.5%

0

Other Killed

Prior: 00.0%

19

Pedestrians Injured

Prior: 27-29.6%

9

Cyclists Injured

Prior: 18-50.0%

1,102

Motorists Injured

Prior: 1,0217.9%

2

Other Injured

Prior: 1100.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · 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 in the peak day of the week between the two periods. In March 2022, Thursday was the busiest day with 667 crashes, whereas Monday was the peak in March 2021 with 662 crashes. The peak hour for crashes remained the 4 PM hour in both periods, although the volume in that hour decreased from 315 incidents in the prior year to 283 in the current year.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased year-over-year, with the fatal crash rate rising from 0.49% in March 2021 to 0.64% in March 2022, corresponding to an increase in fatal crashes from 17 to 23. While the share of serious injury crashes remained stable at approximately 2.1-2.2%, minor injury crashes grew from 8.2% to 8.9% of all incidents. Conversely, crashes involving possible injuries decreased as a proportion of the total, from 17.6% to 16.3%.

Severity is per crash event (most severe injury). 23 fatal crash events resulted in 28 persons killed.

Outcome by Severity (Crash Events)

Fatal23fatal crashes0.6%
35.3%prior 17
Serious Injury77serious injury crashes2.1%
1.3%prior 76
Minor Injury322minor injury crashes8.9%
12.6%prior 286
Possible Injury585possible injury crashes16.3%
-5.2%prior 617
No Injury2,591no injury crashes72%
3.3%prior 2,509

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors remained largely consistent year-over-year, with 'Animal' being the top cause in both periods, increasing from 449 to 477 incidents. 'Followed too close' was a top factor in both years, though its count decreased from 351 to 301. 'Ran off road - left' incidents increased from 211 to 244. Notably, crashes attributed to 'Driving too fast for conditions' saw a significant increase in count, rising from 108 in March 2021 to 151 in March 2022.

Officer-Reported Primary Contributing Cause

Animal477 (13.3%)6.2%prior 449
Followed too close301 (8.4%)-14.2%prior 351
Ran off road - left244 (6.8%)15.6%prior 211
Other (explain in narrative): Other211 (5.9%)5.0%prior 201
Lost Control201 (5.6%)1.5%prior 198
FTYROW: From stop sign192 (5.3%)4.3%prior 184
Driving too fast for conditions151 (4.2%)39.8%prior 108
FTYROW: Making left turn145 (4%)-4.6%prior 152
Ran Traffic Signal139 (3.9%)-4.1%prior 145
Ran off road - straight138 (3.8%)25.5%prior 110

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in clear weather and on dry roads decreased year-over-year. Crashes in clear weather fell from a 61.0% share to a 56.0% share of the total, while crashes on dry surfaces dropped from 71.8% to 64.6%. Correspondingly, there was an increase in crashes during adverse conditions, with incidents on snowy roads rising from 51 to 178 and on wet roads from 415 to 523. The share of crashes happening in daylight decreased from 63.3% to 60.5%.

Weather

Clear2,015 (63.0%)
-5.8%prior 2,139
Cloudy621 (19.4%)
5.6%prior 588
Rain268 (8.4%)
17.5%prior 228
Snow178 (5.6%)
201.7%prior 59
Freezing rain/drizzle60 (1.9%)
46.3%prior 41
Blowing Snow21 (0.7%)
50.0%prior 14
Severe Winds16 (0.5%)
14.3%prior 14
Fog, smoke, smog14 (0.4%)
-12.5%prior 16
Other (explain in narrative)5 (0.2%)
Sleet, hail2 (0.1%)
-84.6%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · Weather condition at time of crash

Lighting

Daylight2,175 (67.9%)
-2.0%prior 2,219
Dark - roadway lighted519 (16.2%)
16.4%prior 446
Dark - roadway not lighted351 (11.0%)
11.8%prior 314
Dusk79 (2.5%)
-12.2%prior 90
Dawn65 (2.0%)
32.7%prior 49
Dark - unknown roadway lighting16 (0.5%)
60.0%prior 10

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · Lighting condition field

Road Surface

Dry2,324 (72.5%)
-7.7%prior 2,518
Wet523 (16.3%)
26.0%prior 415
Snow175 (5.5%)
243.1%prior 51
Ice/frost98 (3.1%)
197.0%prior 33
Gravel46 (1.4%)
21.1%prior 38
Slush27 (0.8%)
-46.0%prior 50
Mud, dirt8 (0.2%)
33.3%prior 6
Sand3 (0.1%)
-40.0%prior 5
Other (explain in narrative)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet variants leading in both March 2022 and March 2021. The count of Ford vehicles involved increased from 927 to 993, while the top Chevrolet category ('CHEV') rose from 683 to 866. In terms of persons involved, the 26-34 age group represented the largest cohort in both periods, growing from 1,256 individuals in the prior year to 1,307 in the current year.

Top Vehicle Makes (6,121 vehicles)

1
FORD993 (16.2%)
7.1%prior 927
2
CHEV866 (14.1%)
26.8%prior 683
3
CHEVROLET323 (5.3%)
-37.0%prior 513
4
TOYT282 (4.6%)
24.8%prior 226
5
DODG263 (4.3%)
38.4%prior 190
6
GMC233 (3.8%)
22.6%prior 190
7
JEEP230 (3.8%)
-0.4%prior 231
8
HOND216 (3.5%)
32.5%prior 163
9
NISS191 (3.1%)
35.5%prior 141
10
NR172 (2.8%)
-9.5%prior 190

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · Vehicle unit records

1,073 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (5,470 persons with recorded sex)

Male3,129 (57.2%)
2.3%prior 3,058
Female2,341 (42.8%)
4.5%prior 2,240

Source: Iowa Crash Data · ArcGIS Open Data · 2022-03-01 to 2022-03-31 · 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: 2022-03-01 through 2022-03-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2022-03-01 through 2022-03-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 3,598
  • Total persons involved: 8,197
  • Total vehicles involved: 6,121

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 2022." Published September 9, 2026. Reporting period: 2022-03-01 to 2022-03-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/march-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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