Yearly Traffic Safety Analysis

287 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Cass County recorded 287 total crashes, a 1.8% increase from the 282 crashes reported in 2021. While total crashes remained relatively stable and fatalities decreased from 4 to 2, the most significant change was a 155.6% increase in crashes involving a driver under the influence (DUI), which rose from 9 in 2021 to 23 in 2022.

287

1.8%was 282

Total Crash Events

2

-50.0%was 4

Persons Killed

96

-2.0%was 98

Persons Injured

2

-33.3%was 3

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

The overall crash trend in Cass County remained relatively stable year-over-year, with total crashes increasing by 1.8% from 282 in 2021 to 287 in 2022. Despite this slight rise in total incidents, key outcomes improved, with total fatalities decreasing by 50% (from 4 to 2) and total injuries seeing a small 2% reduction (from 98 to 96).

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

2

Motorists Killed

Prior: 4-50.0%

0

Other Killed

Prior: 00.0%

1

Pedestrians Injured

Prior: 0%

2

Cyclists Injured

Prior: 0%

91

Motorists Injured

Prior: 98-7.1%

2

Other Injured

Prior: 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 showed consistency year-over-year, with Friday remaining the peak day for crashes in both 2022 (57 crashes) and 2021 (60 crashes). The peak hour of activity shifted slightly from 2 p.m. in 2021 (27 crashes) to 3 p.m. in 2022 (26 crashes). The afternoon hours remained the highest frequency period for collisions in both years.

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

While the number of fatal crashes decreased from 3 in 2021 to 2 in 2022, the severity of injury crashes intensified. The share of crashes resulting in a serious injury increased from 2.8% in 2021 to 5.2% in 2022, with the absolute count of such incidents rising from 8 to 15. The proportion of crashes resulting in no injury decreased from 73.8% to 71.8% of all crashes.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.7%
-33.3%prior 3
Serious Injury15serious injury crashes5.2%
87.5%prior 8
Minor Injury36minor injury crashes12.5%
20.0%prior 30
Possible Injury28possible injury crashes9.8%
-15.2%prior 33
No Injury206no injury crashes71.8%
-1.0%prior 208

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 periods, though the count decreased from 63 in 2021 to 47 in 2022. "Lost Control" and "Ran off road - straight" also remained top-ranked factors with stable counts year-over-year. A notable increase was observed in crashes where failure to yield the right-of-way from a stop sign was a factor, with the count more than doubling from 8 in 2021 to 18 in 2022. Conversely, incidents involving following too closely decreased from 18 to 10.

Officer-Reported Primary Contributing Cause

Animal47 (16.4%)-25.4%prior 63
Ran off road - straight36 (12.5%)9.1%prior 33
Lost Control36 (12.5%)2.9%prior 35
FTYROW: From stop sign18 (6.3%)125.0%prior 8
Other (explain in narrative): Other17 (5.9%)41.7%prior 12
Ran off road - left17 (5.9%)88.9%prior 9
Driving too fast for conditions17 (5.9%)6.3%prior 16
Followed too close10 (3.5%)-44.4%prior 18
Ran Stop Sign9 (3.1%)80.0%prior 5
Improper or erratic lane changing8 (2.8%)

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

Road & Environmental Conditions

The majority of crashes in both 2022 and 2021 occurred in clear weather and on dry roads, with the proportion of crashes under these conditions remaining stable year-over-year. There was a shift in crash counts under specific adverse conditions, with incidents on icy or frosty roads increasing from 19 to 25. Crashes in daylight accounted for a slightly higher proportion of the total in 2022 (54.7%) compared to 2021 (51.4%).

Weather

Clear172 (66.7%)
2.4%prior 168
Cloudy37 (14.3%)
23.3%prior 30
Snow23 (8.9%)
187.5%prior 8
Rain11 (4.3%)
-15.4%prior 13
Blowing Snow9 (3.5%)
-43.8%prior 16
Fog, smoke, smog2 (0.8%)
Freezing rain/drizzle1 (0.4%)
Other (explain in narrative)1 (0.4%)
Severe Winds1 (0.4%)
-83.3%prior 6
Blowing sand, soil, dirt1 (0.4%)

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

Lighting

Daylight157 (60.9%)
8.3%prior 145
Dark - roadway not lighted58 (22.5%)
-14.7%prior 68
Dark - roadway lighted24 (9.3%)
41.2%prior 17
Dawn9 (3.5%)
12.5%prior 8
Dusk7 (2.7%)
16.7%prior 6
Dark - unknown roadway lighting3 (1.2%)

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

Road Surface

Dry182 (70.3%)
8.3%prior 168
Ice/frost25 (9.7%)
31.6%prior 19
Wet21 (8.1%)
-25.0%prior 28
Snow18 (6.9%)
-14.3%prior 21
Gravel9 (3.5%)
28.6%prior 7
Mud, dirt2 (0.8%)
Slush2 (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. The number of Freightliner vehicles in crashes saw a notable increase from 18 in 2021 to 27 in 2022. Analysis of persons involved shows the 55-64 age group was the most represented in both years, though their count fell from 90 to 84. Meanwhile, the number of persons in the 35-44 and 65+ age groups involved in crashes increased, rising from 61 to 83 and 66 to 80, respectively.

Top Vehicle Makes (435 vehicles)

1
CHEV72 (16.6%)
33.3%prior 54
2
FORD69 (15.9%)
-2.8%prior 71
3
FREIGHTLINER27 (6.2%)
50.0%prior 18
4
CHEVROLET27 (6.2%)
-12.9%prior 31
5
JEEP20 (4.6%)
81.8%prior 11
6
DODG20 (4.6%)
150.0%prior 8
7
BUIC13 (3%)
62.5%prior 8
8
KIA11 (2.5%)
9
DODGE11 (2.5%)
-8.3%prior 12
10
NR10 (2.3%)
100.0%prior 5

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

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

Sex Distribution (394 persons with recorded sex)

Male267 (67.8%)
16.6%prior 229
Female127 (32.2%)
4.1%prior 122

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: 287
  • Total persons involved: 594
  • Total vehicles involved: 435

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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