Yearly Traffic Safety Analysis

85 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Shelby County, a total of 85 vehicle crashes were recorded in 2022, a 2.4% increase from the 83 crashes reported in 2021. While the overall crash volume remained relatively stable, the most significant year-over-year change was the occurrence of two fatal crashes resulting in two fatalities in 2022, whereas none were recorded in the prior year. Total injuries saw a slight decrease from 30 in 2021 to 28 in 2022.

85

▲ 2.4%was 83

Total Crash Events

2

Persons Killed

28

▼ -6.7%was 30

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 Shelby County show a slight increase in total incidents, rising from 83 in 2021 to 85 in 2022. This period saw a notable negative development with the introduction of two fatalities after having zero in the previous year. Conversely, the number of people injured in crashes decreased slightly from 30 to 28.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 0→%

28

Motorists Injured

Prior: 30↓-6.7%

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 shifted between the two periods. In 2022, the peak day for crashes was Friday with 15 incidents, a change from 2021 when Tuesday was the peak day with 17 incidents. The peak hour also shifted, with 2022 showing a peak of 7 crashes each during the 3 p.m. and 4 p.m. hours, compared to a more pronounced peak of 14 crashes in the 3 p.m. hour in 2021.

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 recording of two fatal crashes, which accounted for 2.4% of all incidents, compared to zero fatal crashes in 2021. The number of serious injury crashes decreased from two in 2021 to one in 2022. The count of minor injury crashes and possible injury crashes remained unchanged at 11 and 9 respectively, representing a slight proportional decrease relative to the total number of crashes.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.4%
Serious Injury1serious injury crashes1.2%
↓-50.0%prior 2
Minor Injury11minor injury crashes12.9%
→0.0%prior 11
Possible Injury9possible injury crashes10.6%
→0.0%prior 9
No Injury62no injury crashes72.9%
↑1.6%prior 61

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 for crashes shifted significantly year-over-year. In 2022, collisions involving an "Animal" became the primary factor, with the count more than doubling from 10 to 23, a 130% increase. In contrast, crashes attributed to "Lost Control" decreased by 60%, from 10 incidents in 2021 to 4 in 2022. "Ran Stop Sign" incidents also decreased from 7 in 2021 to 4 in 2022, while "Ran off road - left" emerged as a top factor in 2022 with 6 incidents, up from 2 in the prior year.

Officer-Reported Primary Contributing Cause

Animal23 (27.1%)↑130.0%prior 10
FTYROW: From stop sign6 (7.1%)↑20.0%prior 5
Ran off road - left6 (7.1%)
Other (explain in narrative): Other6 (7.1%)↓-33.3%prior 9
Lost Control4 (4.7%)↓-60.0%prior 10
Other (explain in narrative): No improper action4 (4.7%)
Ran Stop Sign4 (4.7%)↓-42.9%prior 7
Driver Distraction: Other interior distraction3 (3.5%)
Driving too fast for conditions3 (3.5%)
FTYROW: Making left turn2 (2.4%)

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

Road & Environmental Conditions

There was a noticeable shift in crash conditions between the two years. Crashes during "Daylight" decreased from 53 in 2021 to 44 in 2022, and incidents on "Dry" road surfaces fell from 52 to 49. Conversely, crashes occurring in adverse weather conditions, including rain and snow, increased from 3 incidents in 2021 to 8 in 2022. Similarly, crashes on "Wet" road surfaces increased from 5 to 8 over the same period.

Weather

Clear48 (77.4%)
↓-14.3%prior 56
Cloudy5 (8.1%)
↓-64.3%prior 14
Rain4 (6.5%)
Snow3 (4.8%)
Fog, smoke, smog1 (1.6%)
Freezing rain/drizzle1 (1.6%)

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

Lighting

Daylight44 (66.7%)
↓-17.0%prior 53
Dark - roadway not lighted9 (13.6%)
↓-30.8%prior 13
Dark - roadway lighted5 (7.6%)
Dark - unknown roadway lighting4 (6.1%)
Dawn3 (4.5%)
Dusk1 (1.5%)

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

Road Surface

Dry49 (74.2%)
↓-5.8%prior 52
Wet8 (12.1%)
↑60.0%prior 5
Gravel4 (6.1%)
↓-42.9%prior 7
Snow3 (4.5%)
Ice/frost1 (1.5%)
↓-83.3%prior 6
Mud, dirt1 (1.5%)

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

Vehicles & Demographics

Vehicle and person demographics show some changes year-over-year. Ford remained the most common vehicle make involved in crashes in both years, though its involvement decreased from 36 vehicles in 2021 to 27 in 2022. Conversely, Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET') saw an increase from 22 to 29. Among persons involved in crashes, there was an increase in the 16-20 age group (from 24 to 31 individuals) and the 65+ age group (from 31 to 34 individuals).

Top Vehicle Makes (133 vehicles)

1
FORD27 (20.3%)
↓-25.0%prior 36
2
CHEV22 (16.5%)
↑57.1%prior 14
3
CHEVROLET7 (5.3%)
↓-12.5%prior 8
4
JEEP6 (4.5%)
5
DODG6 (4.5%)
↓-25.0%prior 8
6
CHRY6 (4.5%)
7
TOYT5 (3.8%)
8
BUICK4 (3%)
9
NISS4 (3%)
10
PONT4 (3%)

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

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

Sex Distribution (121 persons with recorded sex)

Male72 (59.5%)
↑14.3%prior 63
Female49 (40.5%)
↑8.9%prior 45

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: 85
  • Total persons involved: 190
  • Total vehicles involved: 133

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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