Yearly Traffic Safety Analysis

62 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Van Buren County recorded 62 vehicle crashes, a 6.9% increase from the 58 crashes reported in 2021. The most significant change year-over-year was the emergence of traffic fatalities, with two deaths occurring in 2022 compared to none in the prior year.

62

▲ 6.9%was 58

Total Crash Events

2

Persons Killed

31

▼ -8.8%was 34

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 Van Buren County showed a slight increase in 2022. The total number of crashes rose by four, from 58 in 2021 to 62 in 2022. While the number of injuries saw a small decrease from 34 to 31, the county experienced two fatal crashes resulting in two deaths, whereas none were recorded in 2021.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

30

Motorists Injured

Prior: 34↓-11.8%

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 significantly between the two periods. In 2022, Saturday was the most frequent day for crashes with 18 incidents, a stark contrast to 2021 when Tuesday was the peak day with 12 crashes. Similarly, the peak hour for collisions moved from the afternoon at 3 p.m. in 2021 (9 crashes) to late morning at 11 a.m. in 2022 (8 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 worsened in 2022 with the introduction of two fatal incidents, which accounted for 3.2% of all crashes, compared to zero fatal crashes in 2021. The proportion of crashes resulting in serious injuries decreased from 8.6% to 6.5%, and minor injury crashes fell from 27.6% to 19.4%. Conversely, crashes involving possible injuries saw their share increase from 8.6% in 2021 to 16.1% in 2022.

Outcome by Severity (Crash Events)

Fatal2fatal crashes3.2%
Serious Injury4serious injury crashes6.5%
↓-20.0%prior 5
Minor Injury12minor injury crashes19.4%
↓-25.0%prior 16
Possible Injury10possible injury crashes16.1%
↑100.0%prior 5
No Injury34no injury crashes54.8%
↑6.3%prior 32

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 remained largely consistent year-over-year, though their counts shifted. 'Lost Control' was the top factor in both periods, decreasing slightly from 13 incidents in 2021 to 12 in 2022. Crashes involving an 'Animal' increased from 8 to 9 incidents, making it the second-most cited factor in 2022. Incidents of 'Failure to Yield Right of Way from a stop sign' were halved, dropping from 6 in 2021 to 3 in 2022.

Officer-Reported Primary Contributing Cause

Lost Control12 (19.4%)↓-7.7%prior 13
Animal9 (14.5%)↑12.5%prior 8
Ran off road - straight6 (9.7%)↑20.0%prior 5
Ran off road - left4 (6.5%)
FTYROW: Other (explain in narrative)3 (4.8%)
FTYROW: From stop sign3 (4.8%)↓-50.0%prior 6
Ran Stop Sign2 (3.2%)
Driver Distraction: Other interior distraction2 (3.2%)
Driver Distraction: Reaching for object(s)/fallen object(s)2 (3.2%)
Driving too fast for conditions2 (3.2%)

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

Road & Environmental Conditions

Crashes in both years predominantly occurred in clear weather and daylight on dry roads. However, there was a notable increase in crashes on adverse road surfaces in 2022. Collisions on wet roads more than doubled, rising from 3 incidents in 2021 to 8 in 2022. Similarly, crashes on icy or frosty surfaces increased from 1 to 3.

Weather

Clear38 (65.5%)
↓-5.0%prior 40
Cloudy15 (25.9%)
↑25.0%prior 12
Rain3 (5.2%)
Freezing rain/drizzle1 (1.7%)
Snow1 (1.7%)

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

Lighting

Daylight41 (70.7%)
↑17.1%prior 35
Dark - roadway not lighted13 (22.4%)
↑8.3%prior 12
Dark - roadway lighted3 (5.2%)
Dawn1 (1.7%)

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

Road Surface

Dry41 (70.7%)
↓-10.9%prior 46
Wet8 (13.8%)
Ice/frost3 (5.2%)
Snow3 (5.2%)
Gravel2 (3.4%)
Mud, dirt1 (1.7%)

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

Vehicles & Demographics

Ford-made vehicles were involved in the most crashes in 2022 with 20 incidents, up from 12 in the prior year. Chevrolet vehicles, the top make in 2021 with 21 involvements, saw their count decrease to 16 in 2022. Analysis of persons involved in crashes shows a significant increase in the 45-54 age group, which grew from 12 individuals in 2021 to 23 in 2022. The 21-25 age group also saw a notable rise, from 4 to 14 persons involved.

Top Vehicle Makes (85 vehicles)

1
FORD20 (23.5%)
↑66.7%prior 12
2
CHEV13 (15.3%)
↑8.3%prior 12
3
DODG7 (8.2%)
4
TOYT5 (5.9%)
5
DODGE4 (4.7%)
↓-20.0%prior 5
6
GMC4 (4.7%)
↓-42.9%prior 7
7
CHEVROLET3 (3.5%)
↓-66.7%prior 9
8
NISS3 (3.5%)
9
JEP2 (2.4%)
10
KIA2 (2.4%)

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

Sex Distribution (85 persons with recorded sex)

Male51 (60.0%)
↑13.3%prior 45
Female34 (40.0%)
↑30.8%prior 26

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: 62
  • Total persons involved: 123
  • Total vehicles involved: 85

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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