Yearly Traffic Safety Analysis

148 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Greene County recorded 148 total crashes, a 4.5% decrease from the 155 crashes reported in 2021. The most notable year-over-year change was the reduction in traffic fatalities, which fell from 3 in the prior period to 0 in the current period. While total crashes saw a slight decline, the number of people injured increased from 37 to 46.

148

▼ -4.5%was 155

Total Crash Events

0

▼ -100.0%was 3

Persons Killed

46

▲ 24.3%was 37

Persons Injured

0

▼ -100.0%was 3

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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 crashes in Greene County showed a slight year-over-year decline, falling by 4.5% from 155 in 2021 to 148 in 2022. Despite the drop in total collisions and a complete elimination of fatal crashes (from 3 to 0), the number of injuries rose by 24.3%, increasing from 37 to 46.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 3↓-100.0%

46

Motorists Injured

Prior: 37↑24.3%

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 shifted between the two periods. In 2022, the peak day for crashes was Friday with 31 incidents, a change from 2021 when Tuesday was the peak day with 29 incidents. The peak hour also moved from 1 p.m. in the prior period (15 crashes) to the 5 p.m. evening commute hour in the current period (18 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 saw a significant improvement, with fatal crashes dropping from 3 in 2021 to 0 in 2022. However, the proportion of crashes involving injuries increased. The share of serious injury crashes rose from 2.6% to 4.1% of all incidents, and minor injury crashes increased from a 7.7% share to 10.8%. Consequently, the share of crashes with no injuries decreased from 79.4% in the prior period to 73.0% in the current period.

Outcome by Severity (Crash Events)

Serious Injury6serious injury crashes4.1%
↑50.0%prior 4
Minor Injury16minor injury crashes10.8%
↑33.3%prior 12
Possible Injury18possible injury crashes12.2%
↑38.5%prior 13
No Injury108no injury crashes73%
↓-12.2%prior 123

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 involving an animal remained the top contributing factor in both periods, with 49 crashes in 2022 compared to 50 in 2021. The number of crashes attributed to 'Ran Stop Sign' more than doubled, increasing from 4 to 9 incidents. Conversely, crashes involving 'Ran off road - left' decreased in count from 11 in the prior period to 7 in the current period. 'Lost Control' and 'FTYROW: At uncontrolled intersection' both saw a minor increase, from 8 to 9 crashes each.

Officer-Reported Primary Contributing Cause

Animal49 (33.1%)↓-2.0%prior 50
Lost Control9 (6.1%)↑12.5%prior 8
Ran Stop Sign9 (6.1%)
FTYROW: At uncontrolled intersection9 (6.1%)↑12.5%prior 8
Driver Distraction: Other interior distraction8 (5.4%)↑33.3%prior 6
FTYROW: From stop sign8 (5.4%)↑60.0%prior 5
Followed too close7 (4.7%)↑40.0%prior 5
Ran off road - left7 (4.7%)↓-36.4%prior 11
Other (explain in narrative): Other6 (4.1%)↑20.0%prior 5
Driving too fast for conditions4 (2.7%)

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 on dry roads increased from 80 to 87, representing 58.8% of all crashes in 2022 compared to 51.6% in 2021. Correspondingly, crashes on wet surfaces decreased significantly, from 16 incidents to 7. Crashes in clear weather conditions remained the majority but saw a slight decrease from 92 to 84 incidents. The number of crashes occurring during daylight hours also fell from 77 to 67.

Weather

Clear84 (76.4%)
↓-8.7%prior 92
Cloudy19 (17.3%)
↑18.8%prior 16
Snow3 (2.7%)
Rain2 (1.8%)
↓-60.0%prior 5
Other (explain in narrative)1 (0.9%)
Blowing Snow1 (0.9%)

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

Lighting

Daylight67 (61.5%)
↓-13.0%prior 77
Dark - roadway not lighted23 (21.1%)
↓-8.0%prior 25
Dark - roadway lighted7 (6.4%)
↓-12.5%prior 8
Dusk5 (4.6%)
Dawn4 (3.7%)
Dark - unknown roadway lighting3 (2.8%)

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

Road Surface

Dry87 (78.4%)
↑8.8%prior 80
Wet7 (6.3%)
↓-56.3%prior 16
Snow7 (6.3%)
↑40.0%prior 5
Gravel5 (4.5%)
Ice/frost4 (3.6%)
↓-71.4%prior 14
Mud, dirt1 (0.9%)

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

Vehicles & Demographics

Chevrolet and Ford vehicles were the most frequently involved in crashes in both periods, with Chevrolet's involvement remaining steady (45 in 2022 vs. 47 in 2021) while Ford's increased from 33 to 41. An analysis of persons involved in crashes shows a notable shift in age demographics. The number of individuals aged 21-25 more than doubled, from 9 to 21, and those in the 55-64 age group increased from 39 to 57. Conversely, involvement for the 26-34 age group decreased from 54 to 45.

Top Vehicle Makes (215 vehicles)

1
CHEV45 (20.9%)
↓-4.3%prior 47
2
FORD41 (19.1%)
↑24.2%prior 33
3
DODG16 (7.4%)
↑45.5%prior 11
4
JEEP12 (5.6%)
↑50.0%prior 8
5
TOYT9 (4.2%)
→0.0%prior 9
6
BUIC8 (3.7%)
↑60.0%prior 5
7
CHEVROLET7 (3.3%)
↓-53.3%prior 15
8
GMC5 (2.3%)
↓-28.6%prior 7
9
NISSAN5 (2.3%)
10
LINC4 (1.9%)

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

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

Sex Distribution (207 persons with recorded sex)

Male119 (57.5%)
↑2.6%prior 116
Female88 (42.5%)
↑29.4%prior 68

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: 148
  • Total persons involved: 310
  • Total vehicles involved: 215

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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