Yearly Traffic Safety Analysis

154 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Emmet County recorded 154 total vehicle crashes, a 13.0% decrease from the 177 crashes reported in 2021. During this period, total fatalities also decreased from 4 to 3, while total injuries remained nearly stable, increasing from 29 to 30. A notable year-over-year shift was the doubling of crashes attributed to failure to yield from a stop sign, which increased from 5 incidents in 2021 to 10 in 2022.

154

▼ -13.0%was 177

Total Crash Events

3

▼ -25.0%was 4

Persons Killed

30

▲ 3.4%was 29

Persons Injured

3

▼ -25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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, Emmet County saw a downward trend in traffic incidents in 2022 compared to the previous year. The total number of crashes decreased by 13.0%, from 177 in 2021 to 154 in 2022. Fatalities also declined from 4 to 3, although the total number of persons injured saw a slight increase from 29 to 30.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 4↓-25.0%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

28

Motorists Injured

Prior: 29↓-3.4%

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 in Emmet County showed some shifts between 2021 and 2022. While Wednesday remained a peak day for incidents (27 crashes in 2022 vs. 28 in 2021), the peak hour for collisions shifted an hour later, from 6 a.m. in 2021 (12 crashes) to 7 a.m. in 2022 (16 crashes). Crashes on Fridays, which tied for the peak day in 2021 with 28 incidents, decreased to 23 in 2022.

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

The severity of crashes shifted slightly year-over-year, with a decrease in the most severe outcomes. The number of fatal crashes fell from 4 in 2021 to 3 in 2022, and their share of all crashes dropped from 2.3% to 1.9%. Conversely, the proportion of crashes resulting in any level of injury (serious, minor, or possible) increased from 15.8% of all crashes in 2021 to 19.4% in 2022. This was primarily driven by an increase in 'possible injury' crashes, which rose from 11 to 14 incidents.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.9%
↓-25.0%prior 4
Serious Injury1serious injury crashes0.6%
↓-50.0%prior 2
Minor Injury15minor injury crashes9.7%
→0.0%prior 15
Possible Injury14possible injury crashes9.1%
↑27.3%prior 11
No Injury121no injury crashes78.6%
↓-16.6%prior 145

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 leading contributing factor in both periods, accounting for an identical 44 crashes in 2022 and 2021. The ranking of other top factors changed, with crashes due to 'Failure to yield row from stop sign' doubling in count from 5 incidents in 2021 to 10 in 2022. In contrast, crashes attributed to 'Lost Control' decreased by 35.7%, falling from 14 incidents in 2021 to 9 in 2022.

Officer-Reported Primary Contributing Cause

Animal44 (28.6%)→0.0%prior 44
Other (explain in narrative): Other12 (7.8%)↓-7.7%prior 13
FTYROW: From stop sign10 (6.5%)↑100.0%prior 5
Ran off road - left9 (5.8%)↑12.5%prior 8
Lost Control9 (5.8%)↓-35.7%prior 14
Driving too fast for conditions7 (4.5%)↓-22.2%prior 9
Ran Stop Sign5 (3.2%)
Ran off road - straight5 (3.2%)↓-16.7%prior 6
Driver Distraction: Other interior distraction5 (3.2%)→0.0%prior 5
FTYROW: From driveway4 (2.6%)↓-20.0%prior 5

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

Road & Environmental Conditions

While the majority of crashes in both 2022 and 2021 occurred in clear weather and on dry roads, there was a shift in the prevalence of adverse conditions. The proportion of crashes happening in snowy weather doubled, accounting for 7.8% of crashes in 2022 compared to 3.9% in 2021. Similarly, collisions on snow, ice, or slush-covered roads made up a slightly larger share of the total in 2022 (14.3%) than in 2021 (12.4%), even though the absolute number of such incidents was identical at 22.

Weather

Clear91 (76.5%)
↓-15.0%prior 107
Cloudy12 (10.1%)
↓-36.8%prior 19
Snow10 (8.4%)
↑100.0%prior 5
Blowing Snow2 (1.7%)
Freezing rain/drizzle1 (0.8%)
↓-83.3%prior 6
Rain1 (0.8%)
Severe Winds1 (0.8%)
Fog, smoke, smog1 (0.8%)

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

Lighting

Daylight84 (69.4%)
↓-8.7%prior 92
Dark - roadway not lighted19 (15.7%)
↓-29.6%prior 27
Dark - roadway lighted13 (10.7%)
↓-23.5%prior 17
Dawn4 (3.3%)
↓-50.0%prior 8
Dusk1 (0.8%)

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

Road Surface

Dry91 (73.4%)
↓-15.7%prior 108
Snow13 (10.5%)
↑18.2%prior 11
Ice/frost8 (6.5%)
↓-20.0%prior 10
Wet5 (4.0%)
↓-54.5%prior 11
Gravel4 (3.2%)
↓-20.0%prior 5
Mud, dirt2 (1.6%)
Slush1 (0.8%)

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 makes in crashes during both periods, although their total counts decreased in 2022 in line with the overall trend. An analysis of persons involved in crashes reveals a notable shift in age demographics. The number of individuals in the 26-34 age group involved in crashes increased from 29 in 2021 to 49 in 2022. Meanwhile, the 65+ age group, while still highly represented, saw its involvement decrease slightly from 51 individuals in 2021 to 48 in 2022.

Top Vehicle Makes (226 vehicles)

1
CHEV43 (19%)
↓-12.2%prior 49
2
FORD26 (11.5%)
↓-29.7%prior 37
3
GMC16 (7.1%)
↓-5.9%prior 17
4
DODG15 (6.6%)
↑66.7%prior 9
5
CHEVROLET13 (5.8%)
↓-59.4%prior 32
6
CHRY9 (4%)
7
BUIC9 (4%)
↑12.5%prior 8
8
TOYO8 (3.5%)
↓-33.3%prior 12
9
NR8 (3.5%)
↓-11.1%prior 9
10
HOND7 (3.1%)

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

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

Sex Distribution (192 persons with recorded sex)

Male113 (58.9%)
→0.0%prior 113
Female79 (41.1%)
↑2.6%prior 77

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: 154
  • Total persons involved: 318
  • Total vehicles involved: 226

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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