Yearly Traffic Safety Analysis

192 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Grundy County, total traffic crashes decreased from 210 in 2021 to 192 in 2022, an 8.6% reduction. The most notable year-over-year change was the elimination of traffic fatalities, which dropped from 3 in the prior period to zero in the current period. Correspondingly, total injuries also declined from 78 to 60.

192

▼ -8.6%was 210

Total Crash Events

0

▼ -100.0%was 3

Persons Killed

60

▼ -23.1%was 78

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 collisions in Grundy County showed a downward trend year-over-year. The total number of crashes fell by 8.6%, from 210 in 2021 to 192 in 2022. This decrease was accompanied by a 23.1% drop in injuries and a complete reduction in fatalities from three to zero.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 3↓-100.0%

60

Motorists Injured

Prior: 76↓-21.1%

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 timing of crashes shifted between the two periods. In 2022, the peak day for crashes was Thursday with 37 incidents, a change from 2021 when Monday was the peak day with 37 incidents. The peak hour also shifted earlier, from 5 p.m. in 2021 (24 crashes) to 3 p.m. in 2022 (17 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 improved significantly from 2021 to 2022. The number of fatal crashes fell from 3 to 0, and the number of persons killed dropped from 3 to 0. The count of minor injury crashes decreased from 32 to 23, while serious injury crashes remained constant at 8 incidents in both years. Consequently, crashes resulting in no injury made up a larger share of the total, increasing from 71.4% in 2021 to 74.5% in 2022.

Outcome by Severity (Crash Events)

Serious Injury8serious injury crashes4.2%
→0.0%prior 8
Minor Injury23minor injury crashes12%
↓-28.1%prior 32
Possible Injury18possible injury crashes9.4%
↑5.9%prior 17
No Injury143no injury crashes74.5%
↓-4.7%prior 150

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 years, increasing slightly in count from 65 in 2021 to 69 in 2022. The second-most cited factor in 2021, 'Driving too fast for conditions,' saw its count decrease by 44.4%, from 18 crashes to 10. Conversely, crashes attributed to 'Ran off road - left' increased from 12 to 17, becoming the second most common factor in 2022 alongside 'Lost Control,' which held steady at 17 crashes in both periods.

Officer-Reported Primary Contributing Cause

Animal69 (35.9%)↑6.2%prior 65
Lost Control17 (8.9%)→0.0%prior 17
Ran off road - left17 (8.9%)↑41.7%prior 12
Ran off road - straight10 (5.2%)↓-9.1%prior 11
Driving too fast for conditions10 (5.2%)↓-44.4%prior 18
Followed too close9 (4.7%)
Other (explain in narrative): Other8 (4.2%)↑33.3%prior 6
FTYROW: From stop sign7 (3.6%)↓-36.4%prior 11
Exceeded authorized speed5 (2.6%)
Ran Stop Sign5 (2.6%)↓-37.5%prior 8

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

Road & Environmental Conditions

Year-over-year, the distribution of crashes across environmental conditions remained broadly similar, though with some shifts in volume. Crashes on dry roads decreased from 95 to 84, and those in clear weather fell from 93 to 73. However, crashes occurring in snow conditions more than doubled, increasing from 7 incidents in 2021 to 18 in 2022. Collisions in daylight decreased from 101 to 89, while crashes in darkness with no roadway lighting also saw a slight reduction from 33 to 29.

Weather

Clear73 (55.7%)
↓-21.5%prior 93
Cloudy27 (20.6%)
↓-3.6%prior 28
Snow18 (13.7%)
↑157.1%prior 7
Blowing Snow4 (3.1%)
↓-42.9%prior 7
Freezing rain/drizzle3 (2.3%)
↓-50.0%prior 6
Rain3 (2.3%)
↓-40.0%prior 5
Other (explain in narrative)1 (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

Daylight89 (67.4%)
↓-11.9%prior 101
Dark - roadway not lighted29 (22.0%)
↓-12.1%prior 33
Dark - roadway lighted7 (5.3%)
↓-12.5%prior 8
Dawn5 (3.8%)
Dusk2 (1.5%)
↓-75.0%prior 8

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

Road Surface

Dry84 (63.6%)
↓-11.6%prior 95
Ice/frost18 (13.6%)
↓-14.3%prior 21
Snow15 (11.4%)
↑15.4%prior 13
Wet9 (6.8%)
↓-18.2%prior 11
Gravel5 (3.8%)
↓-37.5%prior 8
Slush1 (0.8%)

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 were consistent year-over-year, with Chevrolet and Ford leading in both periods. The number of Chevrolets involved increased from 65 to 70, and Fords increased from 43 to 46. Examining the age of persons involved, there was a notable increase in the 65+ age group, which grew from 39 individuals in 2021 to 65 in 2022. The 16-20 age group also saw an increase in involvement, from 49 to 61 persons.

Top Vehicle Makes (266 vehicles)

1
CHEV55 (20.7%)
↑17.0%prior 47
2
FORD46 (17.3%)
↑7.0%prior 43
3
CHEVROLET15 (5.6%)
↓-16.7%prior 18
4
JEEP13 (4.9%)
↑18.2%prior 11
5
DODG9 (3.4%)
→0.0%prior 9
6
TOYO9 (3.4%)
→0.0%prior 9
7
NISS9 (3.4%)
↑28.6%prior 7
8
CHRY7 (2.6%)
↓-22.2%prior 9
9
KIA7 (2.6%)
↓-30.0%prior 10
10
HOND6 (2.3%)
↓-33.3%prior 9

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

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

Sex Distribution (252 persons with recorded sex)

Male148 (58.7%)
↑15.6%prior 128
Female104 (41.3%)
↑11.8%prior 93

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 10, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 192
  • Total persons involved: 396
  • Total vehicles involved: 266

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 10, 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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