Yearly Traffic Safety Analysis

356 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Boone County, total vehicle crashes remained stable year-over-year, with 356 crashes recorded in 2022 compared to 354 in 2021, a marginal increase of 0.6%. While total fatalities were unchanged at two, total injuries saw a slight decrease from 111 to 105. The most notable shift was a 62.5% increase in crashes involving a driver under the influence of alcohol or drugs, which rose from 8 incidents in 2021 to 13 in 2022.

356

▲ 0.6%was 354

Total Crash Events

2

Persons Killed

105

▼ -5.4%was 111

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 trends in Boone County were stable between 2021 and 2022. The total number of crashes increased by only two incidents, from 354 to 356. Similarly, the number of people killed remained unchanged at two, while the number of people injured decreased by 5.4%, from 111 to 105.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

2

Pedestrians Injured

Prior: 1↑100.0%

3

Cyclists Injured

Prior: 1↑200.0%

100

Motorists Injured

Prior: 108↓-7.4%

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 timing of crashes shifted between the two periods. In 2022, the peak days for crashes were Tuesday and Wednesday, each with 58 incidents, a change from 2021 when Friday was the peak day with 71 crashes. The peak hour for collisions also moved from 3 p.m. in 2021 (39 crashes) to 5 p.m. in 2022 (34 crashes), although the 5 p.m. hour also saw 34 crashes in the prior year.

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

The distribution of crash severity saw some changes, though the number of fatal crashes remained constant at two in both 2021 and 2022. The count of serious injury crashes increased from 10 to 15, and minor injury crashes rose from 26 to 31. Conversely, crashes resulting in possible injuries decreased from 54 in 2021 to 35 in 2022, while property-damage-only crashes increased from 262 to 273.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
→0.0%prior 2
Serious Injury15serious injury crashes4.2%
↑50.0%prior 10
Minor Injury31minor injury crashes8.7%
↑19.2%prior 26
Possible Injury35possible injury crashes9.8%
↓-35.2%prior 54
No Injury273no injury crashes76.7%
↑4.2%prior 262

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

Collisions with animals remained the leading contributing factor in both periods, increasing in count from 78 incidents in 2021 to 92 in 2022. The second most common factor in 2022 was failure to yield from a stop sign, which accounted for 27 crashes, down from 34 in the prior year. Notably, crashes where a driver ran off the road to the left more than doubled, increasing from 11 incidents in 2021 to 23 in 2022.

Officer-Reported Primary Contributing Cause

Animal92 (25.8%)↑17.9%prior 78
FTYROW: From stop sign27 (7.6%)↓-20.6%prior 34
Followed too close24 (6.7%)↓-4.0%prior 25
Ran off road - left23 (6.5%)↑109.1%prior 11
Lost Control21 (5.9%)↑23.5%prior 17
Driving too fast for conditions21 (5.9%)↑40.0%prior 15
Ran Stop Sign19 (5.3%)↑26.7%prior 15
FTYROW: Making left turn14 (3.9%)↓-12.5%prior 16
Other (explain in narrative): Other12 (3.4%)↑33.3%prior 9
Operating vehicle in an reckless, erratic, careless, negligent manner10 (2.8%)↓-9.1%prior 11

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

Road & Environmental Conditions

There was a noticeable shift in the conditions under which crashes occurred. In 2022, fewer crashes happened in clear weather (177 vs. 193) and on dry roads (198 vs. 225) compared to 2021. Conversely, crashes during rain increased from 4 to 20, and incidents on wet roads grew from 17 to 31. Crashes on icy or frosty roads also saw an increase from 25 to 34 incidents year-over-year.

Weather

Clear177 (63.2%)
↓-8.3%prior 193
Cloudy49 (17.5%)
↓-14.0%prior 57
Rain20 (7.1%)
Freezing rain/drizzle11 (3.9%)
↓-35.3%prior 17
Snow10 (3.6%)
↑66.7%prior 6
Blowing Snow8 (2.9%)
Fog, smoke, smog3 (1.1%)
Other (explain in narrative)1 (0.4%)
Severe Winds1 (0.4%)

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

Lighting

Daylight198 (70.5%)
↓-7.5%prior 214
Dark - roadway not lighted47 (16.7%)
↑17.5%prior 40
Dark - roadway lighted19 (6.8%)
→0.0%prior 19
Dusk13 (4.6%)
↑85.7%prior 7
Dawn3 (1.1%)
↓-40.0%prior 5
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry198 (70.0%)
↓-12.0%prior 225
Ice/frost34 (12.0%)
↑36.0%prior 25
Wet31 (11.0%)
↑82.4%prior 17
Snow12 (4.2%)
→0.0%prior 12
Gravel7 (2.5%)
↑16.7%prior 6
Mud, dirt1 (0.4%)

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, Ford and Chevrolet, remained consistent across both years, though both saw a decrease in total incidents. Ford-made vehicles were involved in 95 crashes in 2022, down from 102, while Chevrolet vehicles were in 114 crashes, down from 137. A significant demographic shift occurred in the age of persons involved in crashes, with the 65+ age group increasing from 64 individuals in 2021 to 109 in 2022.

Top Vehicle Makes (544 vehicles)

1
FORD95 (17.5%)
↓-6.9%prior 102
2
CHEV85 (15.6%)
↓-4.5%prior 89
3
DODG29 (5.3%)
→0.0%prior 29
4
CHEVROLET29 (5.3%)
↓-39.6%prior 48
5
JEEP26 (4.8%)
↑8.3%prior 24
6
GMC26 (4.8%)
↑23.8%prior 21
7
TOYO21 (3.9%)
↑40.0%prior 15
8
NISS19 (3.5%)
↑90.0%prior 10
9
HOND17 (3.1%)
→0.0%prior 17
10
CHRY16 (2.9%)
↑77.8%prior 9

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

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

Sex Distribution (519 persons with recorded sex)

Male316 (60.9%)
↑10.5%prior 286
Female203 (39.1%)
↑9.7%prior 185

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: 356
  • Total persons involved: 751
  • Total vehicles involved: 544

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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