Yearly Traffic Safety Analysis

325 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Benton County, total traffic crashes remained nearly stable, with 325 incidents in 2022 compared to 324 in 2021, an increase of less than one percent. Despite the stable crash volume, the severity of outcomes saw a notable improvement. The most significant year-over-year shift was a 44.4% decrease in fatalities, which fell from 9 in 2021 to 5 in 2022, accompanied by a 27.2% reduction in total injuries.

325

▲ 0.3%was 324

Total Crash Events

5

▼ -44.4%was 9

Persons Killed

110

▼ -27.2%was 151

Persons Injured

4

▼ -55.6%was 9

Fatal Crash Events

Note: "Persons Killed" (5) 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 trends in Benton County show a stable volume of incidents year-over-year, with a slight increase of just one crash from 324 to 325. However, the severity of these crashes decreased significantly. The number of people killed dropped from 9 to 5, and the number of people injured fell from 151 to 110, indicating a positive trend toward less severe crash outcomes.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 8↓-37.5%

1

Pedestrians Injured

Prior: 0→%

109

Motorists Injured

Prior: 151↓-27.8%

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 temporal patterns of crashes showed some consistency and some shifts between the two periods. Friday remained the most frequent day for crashes in both 2021 (56 crashes) and 2022 (58 crashes). However, the peak hour for collisions shifted from the 3 p.m. hour in 2021, which saw 27 crashes, to the 5 p.m. hour in 2022, which recorded 26 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 improved notably from 2021 to 2022. The number of fatal crashes fell by more than half, from 9 in the prior period to 4 in the current period, causing the fatal crash rate to decrease from 2.78% to 1.23%. The proportion of crashes resulting in any injury also declined, while crashes with no injuries increased their share from 63.6% (206 crashes) of all incidents in 2021 to 73.2% (238 crashes) in 2022.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.2%
↓-55.6%prior 9
Serious Injury19serious injury crashes5.8%
↑5.6%prior 18
Minor Injury38minor injury crashes11.7%
↓-24.0%prior 50
Possible Injury26possible injury crashes8%
↓-36.6%prior 41
No Injury238no injury crashes73.2%
↑15.5%prior 206

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 years, with the count increasing from 68 in 2021 to 82 in 2022. The second-leading factor, 'Lost Control,' saw a significant decrease in incidents, falling from 49 crashes to 36. 'Ran off road - straight' moved into the top three factors in 2022 with 30 incidents, up from 23 in the prior year, replacing 'Driving too fast for conditions' which fell from 26 to 22 incidents.

Officer-Reported Primary Contributing Cause

Animal82 (25.2%)↑20.6%prior 68
Lost Control36 (11.1%)↓-26.5%prior 49
Ran off road - straight30 (9.2%)↑30.4%prior 23
Driving too fast for conditions22 (6.8%)↓-15.4%prior 26
FTYROW: From stop sign18 (5.5%)↓-14.3%prior 21
Followed too close17 (5.2%)↑21.4%prior 14
Operating vehicle in an reckless, erratic, careless, negligent manner13 (4%)↑44.4%prior 9
Ran off road - left13 (4%)↑18.2%prior 11
Ran Stop Sign12 (3.7%)↑50.0%prior 8
Driver Distraction: Other interior distraction8 (2.5%)↓-11.1%prior 9

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

Road & Environmental Conditions

Comparatively, road conditions during crashes showed some shifts between the two periods. Crashes occurring in snowy weather conditions doubled from 10 in 2021 to 20 in 2022. Conversely, crashes on road surfaces covered in snow were cut in half, dropping from 28 to 14. Incidents on dry road surfaces remained the most common scenario in both years but saw a slight decrease from 183 to 175. Crashes in daylight and dark, unlighted conditions remained proportionally similar year-over-year.

Weather

Clear182 (70.8%)
↑1.7%prior 179
Cloudy38 (14.8%)
↓-15.6%prior 45
Snow20 (7.8%)
↑100.0%prior 10
Rain9 (3.5%)
↓-30.8%prior 13
Freezing rain/drizzle3 (1.2%)
↓-57.1%prior 7
Blowing Snow3 (1.2%)
↓-70.0%prior 10
Other (explain in narrative)1 (0.4%)
Fog, smoke, smog1 (0.4%)
↓-83.3%prior 6

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

Lighting

Daylight161 (62.6%)
↑1.9%prior 158
Dark - roadway not lighted66 (25.7%)
↓-8.3%prior 72
Dark - roadway lighted13 (5.1%)
↓-38.1%prior 21
Dawn9 (3.5%)
↑12.5%prior 8
Dusk7 (2.7%)
↓-36.4%prior 11
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

Dry175 (68.1%)
↓-4.4%prior 183
Gravel22 (8.6%)
↑29.4%prior 17
Ice/frost20 (7.8%)
↑33.3%prior 15
Wet17 (6.6%)
↓-22.7%prior 22
Snow14 (5.4%)
↓-50.0%prior 28
Slush4 (1.6%)
↓-42.9%prior 7
Mud, dirt4 (1.6%)
Water (standing or moving)1 (0.4%)

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

Vehicles & Demographics

An analysis of vehicles involved in crashes shows a shift in the most common makes; Chevrolet-branded vehicles (CHEV/CHEVROLET) were involved in 108 crashes in 2022, surpassing Ford (73 crashes), which was the top make in 2021 with 85 crashes. Regarding the age of persons involved, there was a notable increase in the 26-34 age group, which accounted for 130 individuals in 2022 compared to 88 in 2021. The representation of most other age groups remained relatively stable.

Top Vehicle Makes (466 vehicles)

1
CHEV86 (18.5%)
↑26.5%prior 68
2
FORD73 (15.7%)
↓-14.1%prior 85
3
TOYT26 (5.6%)
↑23.8%prior 21
4
CHEVROLET22 (4.7%)
↓-29.0%prior 31
5
JEEP22 (4.7%)
↑69.2%prior 13
6
DODG20 (4.3%)
↓-4.8%prior 21
7
HOND17 (3.6%)
↑70.0%prior 10
8
GMC16 (3.4%)
↓-11.1%prior 18
9
NISS14 (3%)
↑40.0%prior 10
10
BUIC13 (2.8%)
↓-7.1%prior 14

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

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

Sex Distribution (449 persons with recorded sex)

Male285 (63.5%)
↑20.3%prior 237
Female164 (36.5%)
↑45.1%prior 113

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: 325
  • Total persons involved: 659
  • Total vehicles involved: 466

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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