Yearly Traffic Safety Analysis

701 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Warren County, total traffic crashes remained nearly stable, with 701 incidents in 2022 compared to 699 in 2021, an increase of less than 1%. While total injuries decreased by approximately 10% from 232 to 209, the most notable year-over-year change was a doubling in fatalities, which rose from 3 in 2021 to 6 in 2022.

701

▲ 0.3%was 699

Total Crash Events

6

▲ 100.0%was 3

Persons Killed

209

▼ -9.9%was 232

Persons Injured

5

▲ 66.7%was 3

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

The overall volume of crashes in Warren County was stable year-over-year, increasing by only two incidents from 699 in 2021 to 701 in 2022. However, the severity of outcomes worsened, as total fatalities doubled from 3 to 6. This occurred alongside a 10% decrease in the total number of people injured, which fell from 232 to 209.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 2↑200.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

4

Cyclists Injured

Prior: 3↑33.3%

204

Motorists Injured

Prior: 226↓-9.7%

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 both consistency and change. Friday remained the peak day for crashes in both 2022 (119 crashes) and 2021 (115 crashes). However, the peak hour for collisions shifted significantly from the morning to the evening commute, moving from 7 a.m. in 2021 (62 crashes) to 5 p.m. in 2022 (56 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 outcomes worsened in 2022 compared to the prior year. The number of fatal crashes increased from 3 to 5, and the fatal crash rate rose from 0.43% to 0.71%. Crashes resulting in serious injuries also grew in both count (from 22 to 27) and proportion (from 3.1% to 3.9%). Conversely, crashes involving possible injuries decreased from 99 to 78.

Severity is per crash event (most severe injury). 5 fatal crash events resulted in 6 persons killed.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.7%
↑66.7%prior 3
Serious Injury27serious injury crashes3.9%
↑22.7%prior 22
Minor Injury68minor injury crashes9.7%
→0.0%prior 68
Possible Injury78possible injury crashes11.1%
↓-21.2%prior 99
No Injury523no injury crashes74.6%
↑3.2%prior 507

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, with the count increasing by 25% from 144 crashes in 2021 to 180 in 2022. Several other top factors saw a decrease in reported incidents. Crashes attributed to 'Lost Control' fell by 11%, from 53 to 47, and incidents of 'Followed too close' dropped by nearly 25%, from 49 to 37.

Officer-Reported Primary Contributing Cause

Animal180 (25.7%)↑25.0%prior 144
Other (explain in narrative): Other50 (7.1%)↑31.6%prior 38
Lost Control47 (6.7%)↓-11.3%prior 53
Ran off road - left41 (5.8%)↑7.9%prior 38
Ran off road - straight39 (5.6%)↑14.7%prior 34
Followed too close37 (5.3%)↓-24.5%prior 49
FTYROW: From stop sign35 (5%)↑2.9%prior 34
FTYROW: Making left turn31 (4.4%)↓-3.1%prior 32
Driver Distraction: Other interior distraction24 (3.4%)↑50.0%prior 16
Driving too fast for conditions23 (3.3%)↓-20.7%prior 29

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 were largely consistent year-over-year, with the majority of incidents in both 2022 and 2021 happening in daylight and on clear days. However, there was a shift in reported road surface conditions. The share of crashes on dry roads decreased from 64.4% in 2021 to 59.5% in 2022, while the count of crashes on gravel roads increased from 18 to 29.

Weather

Clear386 (68.1%)
↓-2.3%prior 395
Cloudy97 (17.1%)
↓-14.9%prior 114
Rain29 (5.1%)
↓-12.1%prior 33
Snow27 (4.8%)
↑22.7%prior 22
Freezing rain/drizzle9 (1.6%)
↑50.0%prior 6
Blowing Snow8 (1.4%)
↑14.3%prior 7
Fog, smoke, smog5 (0.9%)
Severe Winds4 (0.7%)
Other (explain in narrative)2 (0.4%)

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

Lighting

Daylight370 (64.9%)
↓-5.1%prior 390
Dark - roadway not lighted105 (18.4%)
↓-0.9%prior 106
Dark - roadway lighted45 (7.9%)
→0.0%prior 45
Dawn26 (4.6%)
↑52.9%prior 17
Dusk21 (3.7%)
↓-19.2%prior 26
Dark - unknown roadway lighting3 (0.5%)

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

Road Surface

Dry417 (73.2%)
↓-7.3%prior 450
Wet56 (9.8%)
↓-8.2%prior 61
Snow32 (5.6%)
↑14.3%prior 28
Gravel29 (5.1%)
↑61.1%prior 18
Ice/frost26 (4.6%)
↓-7.1%prior 28
Slush8 (1.4%)
Mud, dirt2 (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 but their counts decreased from 2021 to 2022. Ford-involved vehicles dropped from 212 to 172, while Chevrolet-branded vehicles fell from a combined 237 to 209. The demographics of persons involved in crashes also shifted, with a decrease in the 16-20 age group (from 205 to 180) and notable increases in the 26-34 age group (from 221 to 249) and the 45-54 age group (from 167 to 214).

Top Vehicle Makes (1,069 vehicles)

1
FORD172 (16.1%)
↓-18.9%prior 212
2
CHEV160 (15%)
↓-0.6%prior 161
3
JEEP59 (5.5%)
↑78.8%prior 33
4
TOYT56 (5.2%)
↑12.0%prior 50
5
DODG51 (4.8%)
↑13.3%prior 45
6
CHEVROLET49 (4.6%)
↓-35.5%prior 76
7
GMC48 (4.5%)
↑33.3%prior 36
8
HOND34 (3.2%)
↑21.4%prior 28
9
NISS33 (3.1%)
↓-2.9%prior 34
10
KIA29 (2.7%)
↓-6.5%prior 31

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

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

Sex Distribution (985 persons with recorded sex)

Male589 (59.8%)
↑10.3%prior 534
Female396 (40.2%)
↑5.0%prior 377

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: 701
  • Total persons involved: 1,479
  • Total vehicles involved: 1,069

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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