Yearly Traffic Safety Analysis

188 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Sac County, total traffic crashes increased by 27.9% from 147 in 2021 to 188 in 2022. This rise was accompanied by a significant increase in crash severity. The most notable year-over-year shift was a 300% increase in serious injury crashes, which rose from 2 incidents in 2021 to 8 in 2022.

188

▲ 27.9%was 147

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

65

▲ 51.2%was 43

Persons Injured

2

▲ 100.0%was 1

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

Crash data indicates a rising trend in Sac County year-over-year. Total crashes increased from 147 in 2021 to 188 in 2022. Correspondingly, the number of people injured rose by 51.2%, from 43 to 65, and fatalities doubled from one to two.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

63

Motorists Injured

Prior: 43↑46.5%

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. The peak day for crashes moved from Friday in 2021 (25 crashes) to Tuesday in 2022 (35 crashes). The peak hour also changed, shifting from the evening rush at 7 p.m. in the prior year (14 crashes) to the morning, with both 7 a.m. and 10 a.m. recording the highest volume in the current year (15 crashes each).

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 worsened year-over-year. The number of fatal crashes doubled from one to two, and the fatal crash rate per 100 crashes increased from 0.68 to 1.06. Crashes resulting in serious injuries saw a substantial increase, rising from 2 in 2021 to 8 in 2022. Consequently, the share of crashes with no injuries decreased from 76.2% in the prior period to 71.8% in the current period.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.1%
↑100.0%prior 1
Serious Injury8serious injury crashes4.3%
↑300.0%prior 2
Minor Injury20minor injury crashes10.6%
↑53.8%prior 13
Possible Injury23possible injury crashes12.2%
↑21.1%prior 19
No Injury135no injury crashes71.8%
↑20.5%prior 112

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 with animals remained the leading contributing factor in both years, with counts rising slightly from 50 to 52 incidents. Several other factors saw notable count-based increases, including crashes attributed to 'Lost Control' (from 13 to 21), 'Driving too fast for conditions' (from 3 to 9), and 'Followed too close' (from 3 to 8). Conversely, crashes involving 'Failure to Yield Right of Way from a stop sign' decreased from 8 to 5.

Officer-Reported Primary Contributing Cause

Animal52 (27.7%)↑4.0%prior 50
Lost Control21 (11.2%)↑61.5%prior 13
Other (explain in narrative): Other18 (9.6%)↑28.6%prior 14
Ran off road - left11 (5.9%)↑37.5%prior 8
Driving too fast for conditions9 (4.8%)
Ran off road - straight8 (4.3%)↑60.0%prior 5
Followed too close8 (4.3%)
Driver Distraction: Other interior distraction7 (3.7%)↓-12.5%prior 8
FTYROW: At uncontrolled intersection7 (3.7%)
FTYROW: From stop sign5 (2.7%)↓-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

While the majority of crashes in both periods occurred in clear weather on dry roads, there was a shift in lighting conditions. The proportion of crashes happening in daylight increased from 44.9% of all incidents in 2021 to 53.2% in 2022. There was also a significant increase in crashes occurring on gravel road surfaces, with the count more than doubling from 5 in the prior year to 13 in the current year.

Weather

Clear104 (67.1%)
↑36.8%prior 76
Cloudy19 (12.3%)
↓-13.6%prior 22
Fog, smoke, smog7 (4.5%)
Severe Winds7 (4.5%)
Rain7 (4.5%)
→0.0%prior 7
Snow5 (3.2%)
Blowing Snow3 (1.9%)
Freezing rain/drizzle3 (1.9%)

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

Lighting

Daylight100 (64.1%)
↑51.5%prior 66
Dark - roadway not lighted36 (23.1%)
↑12.5%prior 32
Dusk10 (6.4%)
Dark - roadway lighted8 (5.1%)
↓-20.0%prior 10
Dawn1 (0.6%)
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry109 (69.9%)
↑32.9%prior 82
Ice/frost15 (9.6%)
↑36.4%prior 11
Gravel13 (8.3%)
↑160.0%prior 5
Wet11 (7.1%)
↓-15.4%prior 13
Snow5 (3.2%)
Slush3 (1.9%)

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 during both periods. The number of Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET' makes) in crashes increased from 51 to 63 year-over-year, while Ford involvement remained steady. Demographically, the number and proportion of persons aged 35-44 involved in crashes increased, while the share of persons aged 65 and older decreased from 17.7% of all involved persons in 2021 to 11.2% in 2022.

Top Vehicle Makes (258 vehicles)

1
CHEV47 (18.2%)
↑56.7%prior 30
2
FORD37 (14.3%)
↑2.8%prior 36
3
GMC18 (7%)
↑50.0%prior 12
4
CHEVROLET16 (6.2%)
↓-23.8%prior 21
5
DODG15 (5.8%)
↑66.7%prior 9
6
JEEP9 (3.5%)
↑50.0%prior 6
7
FREIGHTLINER8 (3.1%)
8
BUIC7 (2.7%)
↑16.7%prior 6
9
CHRY7 (2.7%)
↑40.0%prior 5
10
PONT6 (2.3%)

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

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

Sex Distribution (236 persons with recorded sex)

Male156 (66.1%)
↑73.3%prior 90
Female80 (33.9%)
↑5.3%prior 76

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: 188
  • Total persons involved: 376
  • Total vehicles involved: 258

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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