Yearly Traffic Safety Analysis

316 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Buena Vista County, total traffic crashes increased from 293 in 2021 to 316 in 2022, representing a 7.8% rise. While the number of crashes and injuries (102, up from 92) saw an increase, the number of fatalities decreased from 3 in the prior period to 1 in the current period. The most notable year-over-year shift was a 50% increase in the count of crashes attributed to losing control, which rose from 14 to 21 incidents.

316

▲ 7.8%was 293

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

102

▲ 10.9%was 92

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 traffic safety trends in Buena Vista County show a slight worsening, with total crashes increasing by 7.8% from 293 to 316 year-over-year. The number of people injured also rose by 10.9%, from 92 to 102. However, this was contrasted by a significant decrease in fatalities, which fell from 3 in 2021 to 1 in 2022.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 3↓-66.7%

2

Pedestrians Injured

Prior: 1↑100.0%

2

Cyclists Injured

Prior: 4↓-50.0%

98

Motorists Injured

Prior: 87↑12.6%

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 temporal patterns of crashes showed some changes between the two periods. While Friday remained the peak day for crashes in both 2022 (56 crashes) and 2021 (57 crashes), the peak hour shifted. In 2022, the most crashes occurred during the 5 p.m. hour with 33 incidents, whereas in 2021, the peak was earlier at the 3 p.m. hour with 25 incidents.

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 severity of crashes shifted year-over-year. The number of fatal crashes decreased from 2 in 2021 to 1 in 2022, and the fatal crash rate fell from 0.68% to 0.32%. The proportion of crashes resulting in serious injury remained stable at approximately 2% in both periods. However, there was a slight increase in the share of crashes involving minor injuries (12.7% in 2022 vs. 11.6% in 2021) and possible injuries (13.0% vs. 12.6%).

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-50.0%prior 2
Serious Injury6serious injury crashes1.9%
→0.0%prior 6
Minor Injury40minor injury crashes12.7%
↑17.6%prior 34
Possible Injury41possible injury crashes13%
↑10.8%prior 37
No Injury228no injury crashes72.2%
↑6.5%prior 214

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 top contributing factor in both 2022 and 2021, with an identical count of 53 incidents. Significant changes occurred in other leading factors; crashes attributed to losing control increased by 50%, from 14 to 21, while incidents of failing to yield from a stop sign rose from 11 to 19. Conversely, crashes from running a stop sign decreased in count from 19 to 8, and incidents of following too closely fell from 18 to 14.

Officer-Reported Primary Contributing Cause

Animal53 (16.8%)→0.0%prior 53
Other (explain in narrative): Other26 (8.2%)↑62.5%prior 16
Lost Control21 (6.6%)↑50.0%prior 14
FTYROW: From stop sign19 (6%)↑72.7%prior 11
Made improper turn16 (5.1%)↑100.0%prior 8
Ran off road - left15 (4.7%)↑114.3%prior 7
Followed too close14 (4.4%)↓-22.2%prior 18
Ran off road - straight14 (4.4%)↑100.0%prior 7
Driving too fast for conditions13 (4.1%)→0.0%prior 13
Improper Backing11 (3.5%)↓-15.4%prior 13

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

Road & Environmental Conditions

The conditions under which crashes occurred showed some year-over-year shifts. While clear weather and dry roads remained the most common conditions in both periods, the number of crashes on snowy road surfaces more than doubled, from 8 in 2021 to 20 in 2022. Similarly, crashes in dark, unlighted conditions increased from 42 to 56. In contrast, crashes on icy or frosty roads decreased from 25 to 21.

Weather

Clear213 (73.7%)
↑11.5%prior 191
Cloudy42 (14.5%)
→0.0%prior 42
Snow12 (4.2%)
↑140.0%prior 5
Blowing Snow7 (2.4%)
Rain7 (2.4%)
→0.0%prior 7
Freezing rain/drizzle3 (1.0%)
↓-40.0%prior 5
Severe Winds3 (1.0%)
Other (explain in narrative)2 (0.7%)

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

Lighting

Daylight191 (66.3%)
↑8.5%prior 176
Dark - roadway not lighted56 (19.4%)
↑33.3%prior 42
Dark - roadway lighted25 (8.7%)
→0.0%prior 25
Dusk11 (3.8%)
↓-8.3%prior 12
Dark - unknown roadway lighting3 (1.0%)
Dawn2 (0.7%)
↓-60.0%prior 5

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

Road Surface

Dry218 (75.4%)
↑13.0%prior 193
Ice/frost21 (7.3%)
↓-16.0%prior 25
Wet20 (6.9%)
↓-16.7%prior 24
Snow20 (6.9%)
↑150.0%prior 8
Gravel8 (2.8%)
↓-11.1%prior 9
Slush2 (0.7%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes saw a shift in rankings. In 2021, Ford was the most common make with 82 vehicles, followed by Chevrolet with a combined 98 (74 CHEV, 24 CHEVROLET). In 2022, Chevrolet became the most frequent make with a combined 113 vehicles (88 CHEV, 25 CHEVROLET), while Ford-made vehicles involved in crashes decreased to 80. The age distribution of persons involved also changed, with the 16-20 and 26-34 age groups seeing an increase in their share of total persons, while the 65+ age group's representation decreased from 10.6% to 8.2%.

Top Vehicle Makes (501 vehicles)

1
CHEV88 (17.6%)
↑18.9%prior 74
2
FORD80 (16%)
↓-2.4%prior 82
3
GMC26 (5.2%)
↑23.8%prior 21
4
DODG25 (5%)
↑19.0%prior 21
5
CHEVROLET25 (5%)
↑4.2%prior 24
6
HOND16 (3.2%)
↑14.3%prior 14
7
BUIC16 (3.2%)
↑33.3%prior 12
8
KIA15 (3%)
↑15.4%prior 13
9
JEEP14 (2.8%)
↑55.6%prior 9
10
TOYT13 (2.6%)
↓-23.5%prior 17

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

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

Sex Distribution (458 persons with recorded sex)

Male270 (59.0%)
↑20.0%prior 225
Female188 (41.0%)
↑27.0%prior 148

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: 316
  • Total persons involved: 668
  • Total vehicles involved: 501

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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