Yearly Traffic Safety Analysis

145 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Lyon County recorded 145 total traffic crashes, a 10.5% decrease from the 162 crashes documented in 2021. While total crashes declined, the number of fatalities remained constant at one, and total injuries saw a slight increase from 51 to 53. The most significant trend was the overall reduction in crash incidents, even as the proportion of crashes involving an injury grew.

145

▼ -10.5%was 162

Total Crash Events

1

Persons Killed

53

▲ 3.9%was 51

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

Traffic crashes in Lyon County showed a downward trend year-over-year, decreasing by 10.5% from 162 incidents in 2021 to 145 in 2022. Despite this reduction in total collisions, the number of resulting injuries increased slightly by 3.9%, from 51 to 53. The number of fatalities remained unchanged, with one death recorded in each period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

3

Pedestrians Injured

Prior: 0→%

50

Motorists Injured

Prior: 51↓-2.0%

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 most frequent days for crashes were Monday and Friday, each with 26 incidents, a change from 2021 when Wednesday was the peak day with 25 crashes. The peak hour for collisions remained the 5 p.m. hour in both years, but the volume of crashes during this time increased from 14 in 2021 to 19 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 distribution of crashes saw a shift towards more injury-involved incidents. While the number of fatal crashes remained constant at one in both 2021 and 2022, the proportion of crashes resulting in some form of injury (serious, minor, or possible) increased from 26.5% in 2021 to 30.3% in 2022. Consequently, the share of crashes with no injuries reported decreased from 72.8% to 69.0% year-over-year.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
→0.0%prior 1
Serious Injury7serious injury crashes4.8%
→0.0%prior 7
Minor Injury17minor injury crashes11.7%
↓-15.0%prior 20
Possible Injury20possible injury crashes13.8%
↑25.0%prior 16
No Injury100no injury crashes69%
↓-15.3%prior 118

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 animals remained the leading contributing factor in both periods, though the count decreased by 13.5% from 52 incidents in 2021 to 45 in 2022. 'Ran off road - straight' became a more prominent factor, with the count of such crashes increasing by 44.4% from 9 to 13. Notably, crashes attributed to 'Failure to Yield Right of Way from a stop sign' doubled in count from 5 to 10, while incidents of 'Followed too close' dropped from 11 to 4.

Officer-Reported Primary Contributing Cause

Animal45 (31%)↓-13.5%prior 52
Ran off road - straight13 (9%)↑44.4%prior 9
Lost Control12 (8.3%)↑9.1%prior 11
Driving too fast for conditions11 (7.6%)→0.0%prior 11
FTYROW: From stop sign10 (6.9%)↑100.0%prior 5
Other (explain in narrative): Other5 (3.4%)
Operating vehicle in an reckless, erratic, careless, negligent manner4 (2.8%)↓-20.0%prior 5
Followed too close4 (2.8%)↓-63.6%prior 11
Driver Distraction: Other interior distraction4 (2.8%)↓-33.3%prior 6
Made improper turn3 (2.1%)

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with most incidents in both periods occurring in clear weather and during daylight on dry roads. However, there were some notable shifts in adverse conditions. Crashes on roads with ice or frost doubled, increasing from 5 in 2021 to 10 in 2022. Additionally, collisions in the dark on unlighted roadways rose from 28 to 33.

Weather

Clear94 (81.0%)
↓-1.1%prior 95
Cloudy6 (5.2%)
↓-40.0%prior 10
Blowing Snow6 (5.2%)
Snow3 (2.6%)
↓-50.0%prior 6
Freezing rain/drizzle2 (1.7%)
Rain2 (1.7%)
↓-60.0%prior 5
Severe Winds2 (1.7%)
Blowing sand, soil, dirt1 (0.9%)

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

Lighting

Daylight76 (65.5%)
↑1.3%prior 75
Dark - roadway not lighted33 (28.4%)
↑17.9%prior 28
Dark - roadway lighted6 (5.2%)
↓-25.0%prior 8
Dusk1 (0.9%)

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

Road Surface

Dry92 (78.6%)
↑3.4%prior 89
Ice/frost10 (8.5%)
↑100.0%prior 5
Wet5 (4.3%)
↓-28.6%prior 7
Snow5 (4.3%)
↓-28.6%prior 7
Gravel3 (2.6%)
↓-66.7%prior 9
Slush2 (1.7%)

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

Vehicles & Demographics

Ford and Chevrolet remained the most common vehicle makes involved in crashes in both years, though their total counts declined in 2022. Ford-involved crashes fell from 43 to 39, while combined Chevrolet and CHEV-branded vehicles dropped from 53 to 40. Analysis of persons involved shows a notable demographic shift, with a significant increase in crash involvement for the 16-20 age group, which rose from 28 individuals in 2021 to 44 in 2022. The 65+ age group also saw an increase in involvement, from 37 to 45 persons.

Top Vehicle Makes (195 vehicles)

1
FORD39 (20%)
↓-9.3%prior 43
2
CHEV27 (13.8%)
↑42.1%prior 19
3
CHEVROLET13 (6.7%)
↓-61.8%prior 34
4
GMC9 (4.6%)
↓-52.6%prior 19
5
DODG8 (4.1%)
↓-11.1%prior 9
6
HOND8 (4.1%)
7
NR7 (3.6%)
8
BUIC6 (3.1%)
↓-25.0%prior 8
9
DODGE5 (2.6%)
↓-16.7%prior 6
10
TOYOTA5 (2.6%)

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

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

Sex Distribution (174 persons with recorded sex)

Male100 (57.5%)
↓-9.1%prior 110
Female74 (42.5%)
↑8.8%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: 145
  • Total persons involved: 290
  • Total vehicles involved: 195

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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