Yearly Traffic Safety Analysis

221 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Carroll County, a total of 221 crashes occurred in 2022, a 3.1% decrease from the 228 crashes recorded in 2021. While the overall crash volume declined, the most notable year-over-year shift was an increase in crash severity, with total fatalities rising from zero in 2021 to four in 2022.

221

▼ -3.1%was 228

Total Crash Events

4

Persons Killed

86

▲ 8.9%was 79

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash totals in Carroll County saw a slight decrease in 2022 compared to the prior year, falling from 228 to 221 incidents. Despite this reduction in frequency, the severity of crashes worsened, as total injuries rose from 79 to 86, and fatalities increased from zero to four.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 0→%

1

Cyclists Injured

Prior: 1→0.0%

85

Motorists Injured

Prior: 77↑10.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The time of day for crashes remained consistent year-over-year, with the afternoon hours seeing the highest frequency in both periods. The specific peak hour shifted slightly from 3 p.m. in 2021 (21 crashes) to 2 p.m. in 2022 (22 crashes). While Friday was the definitive peak day in 2021 with 47 crashes, it was tied with Wednesday in 2022, both recording 40 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased significantly in 2022 compared to 2021. Four fatal crashes occurred in 2022, representing 1.8% of all incidents, whereas no fatal crashes were recorded in the prior year. The proportion of crashes involving any level of injury also grew, from 28.6% of all crashes in 2021 to 32.1% in 2022, with the share of serious injury crashes increasing from 2.2% to 3.6%.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.8%
Serious Injury8serious injury crashes3.6%
↑60.0%prior 5
Minor Injury28minor injury crashes12.7%
↓-6.7%prior 30
Possible Injury35possible injury crashes15.8%
↑16.7%prior 30
No Injury146no injury crashes66.1%
↓-10.4%prior 163

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors shifted between the two periods. In 2021, 'Followed too close' was the top factor with 22 crashes, but its count decreased by 36.4% to 14 crashes in 2022. Conversely, 'Failure to yield right of way from a stop sign' became the top factor in 2022 with 22 crashes, a 29.4% increase in count from 17 crashes in the prior year. Crashes attributed to 'Lost Control' also saw a notable decrease, falling from 19 incidents in 2021 to 10 in 2022.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign22 (10%)↑29.4%prior 17
Other (explain in narrative): Other17 (7.7%)↑6.3%prior 16
Ran off road - left15 (6.8%)↑114.3%prior 7
Followed too close14 (6.3%)↓-36.4%prior 22
Animal11 (5%)↓-26.7%prior 15
Driving too fast for conditions10 (4.5%)↓-9.1%prior 11
Lost Control10 (4.5%)↓-47.4%prior 19
Improper Backing10 (4.5%)↓-16.7%prior 12
Ran Stop Sign9 (4.1%)→0.0%prior 9
FTYROW: Making left turn8 (3.6%)↑60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In both 2022 and 2021, the vast majority of crashes occurred under favorable conditions. The proportion of crashes on dry road surfaces was nearly identical at 69.7% for both years. Crashes in clear weather represented the majority in both periods, accounting for 71.5% of incidents in 2021 and increasing to 75.6% in 2022. Similarly, the share of crashes occurring during daylight hours rose from 65.8% in 2021 to 73.3% in 2022.

Weather

Clear167 (78.0%)
↑2.5%prior 163
Cloudy22 (10.3%)
↓-33.3%prior 33
Snow9 (4.2%)
↑12.5%prior 8
Rain6 (2.8%)
↓-40.0%prior 10
Freezing rain/drizzle6 (2.8%)
Blowing Snow2 (0.9%)
Other (explain in narrative)1 (0.5%)
Fog, smoke, smog1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight162 (75.7%)
↑8.0%prior 150
Dark - roadway not lighted26 (12.1%)
↓-27.8%prior 36
Dark - roadway lighted20 (9.3%)
↓-4.8%prior 21
Dawn4 (1.9%)
↓-42.9%prior 7
Dusk2 (0.9%)
↓-60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry154 (72.0%)
↓-3.1%prior 159
Wet22 (10.3%)
↑4.8%prior 21
Gravel13 (6.1%)
↓-13.3%prior 15
Ice/frost13 (6.1%)
↑30.0%prior 10
Snow9 (4.2%)
↓-35.7%prior 14
Mud, dirt2 (0.9%)
Slush1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet and Ford leading in both years; Chevrolet involvement was stable (113 in 2022 vs. 114 in 2021), while Ford involvement decreased from 66 to 53. Regarding driver age, the 16-20 age group was the most represented in both periods, increasing slightly from 80 to 87 persons. Notably, the 65+ age group saw a significant increase in involvement, rising from 56 persons in 2021 to 81 in 2022.

Top Vehicle Makes (400 vehicles)

1
CHEV84 (21%)
↑42.4%prior 59
2
FORD53 (13.3%)
↓-19.7%prior 66
3
CHEVROLET29 (7.2%)
↓-47.3%prior 55
4
TOYT17 (4.3%)
↑183.3%prior 6
5
JEEP17 (4.3%)
↓-19.0%prior 21
6
CHRY14 (3.5%)
↑16.7%prior 12
7
DODG14 (3.5%)
↑55.6%prior 9
8
NISS14 (3.5%)
9
TOYO13 (3.3%)
↓-7.1%prior 14
10
BUIC12 (3%)
↑33.3%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

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

Sex Distribution (354 persons with recorded sex)

Male195 (55.1%)
↓-1.0%prior 197
Female159 (44.9%)
↑30.3%prior 122

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 221
  • Total persons involved: 534
  • Total vehicles involved: 400

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: 2022." Published September 9, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2022-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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