Yearly Traffic Safety Analysis

372 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Cedar County recorded 372 total crashes, a 1.1% decrease from the 376 crashes reported in 2021. Despite the slight drop in total collisions, the number of people injured increased by 27.9%, rising from 86 in 2021 to 110 in 2022. Fatalities decreased from 4 in the prior year to 3 in the current year.

372

▼ -1.1%was 376

Total Crash Events

3

▼ -25.0%was 4

Persons Killed

110

▲ 27.9%was 86

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 crash volume in Cedar County remained relatively stable, with a slight year-over-year decrease of 1.1%, from 376 crashes in 2021 to 372 in 2022. While total fatalities also decreased from 4 to 3, the number of injuries saw a substantial increase of 27.9% during the same period.

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%

2

Pedestrians Injured

Prior: 2→0.0%

2

Cyclists Injured

Prior: 0→%

106

Motorists Injured

Prior: 84↑26.2%

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 some shifts between the two years. The peak day for crashes moved from Thursday (66 crashes) in 2021 to Friday (70 crashes) in 2022. Similarly, the peak hour for collisions shifted later in the day, from 3 p.m. (28 crashes) in the prior year to 5 p.m. (29 crashes) in the current year.

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 number of fatal crashes decreased from 4 in 2021 to 3 in 2022, with their share of total crashes falling from 1.1% to 0.8%. However, crashes resulting in injuries showed an upward trend. The count of serious injury crashes increased from 7 to 8, and possible injury crashes rose from 25 to 34. Consequently, the proportion of crashes with no injuries decreased from 79.8% in 2021 to 76.9% in 2022.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.8%
↓-25.0%prior 4
Serious Injury8serious injury crashes2.2%
↑14.3%prior 7
Minor Injury41minor injury crashes11%
↑2.5%prior 40
Possible Injury34possible injury crashes9.1%
↑36.0%prior 25
No Injury286no injury crashes76.9%
↓-4.7%prior 300

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

The leading contributing factors remained consistent year-over-year, with 'Animal' being the primary factor in both 2022 (96 crashes) and 2021 (97 crashes). 'Ran off road - straight' (41 vs. 43 crashes) and 'Lost Control' (39 vs. 38 crashes) also held their high rankings. Notably, crashes attributed to 'Driving too fast for conditions' decreased by 45.2% in count, from 31 incidents in 2021 to 17 in 2022. Conversely, crashes involving 'Failure to yield right-of-way from a stop sign' increased by 80% in count, rising from 10 to 18 incidents.

Officer-Reported Primary Contributing Cause

Animal96 (25.8%)↓-1.0%prior 97
Ran off road - straight41 (11%)↓-4.7%prior 43
Lost Control39 (10.5%)↑2.6%prior 38
Followed too close28 (7.5%)↑3.7%prior 27
FTYROW: From stop sign18 (4.8%)↑80.0%prior 10
Driving too fast for conditions17 (4.6%)↓-45.2%prior 31
Ran off road - left16 (4.3%)↑14.3%prior 14
Other (explain in narrative): Other12 (3.2%)
Operating vehicle in an reckless, erratic, careless, negligent manner9 (2.4%)↓-43.8%prior 16
Other (explain in narrative): No improper action9 (2.4%)↑12.5%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

Crash conditions remained largely similar year-over-year, with the majority of incidents in both 2022 and 2021 occurring in clear weather and on dry roads. However, there was a notable shift in lighting conditions. Crashes occurring in 'Dark - roadway not lighted' conditions increased by 34.4%, from 61 incidents in 2021 to 82 in 2022. Crashes on roads with ice or frost decreased from 28 to 21 during the same period.

Weather

Clear189 (67.3%)
↑0.5%prior 188
Cloudy43 (15.3%)
↑2.4%prior 42
Rain18 (6.4%)
↑20.0%prior 15
Snow17 (6.0%)
↓-10.5%prior 19
Blowing Snow8 (2.8%)
↓-20.0%prior 10
Freezing rain/drizzle4 (1.4%)
↓-20.0%prior 5
Other (explain in narrative)1 (0.4%)
Fog, smoke, smog1 (0.4%)

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

Lighting

Daylight175 (61.6%)
↓-5.9%prior 186
Dark - roadway not lighted82 (28.9%)
↑34.4%prior 61
Dark - roadway lighted10 (3.5%)
↓-54.5%prior 22
Dusk8 (2.8%)
↓-20.0%prior 10
Dawn7 (2.5%)
→0.0%prior 7
Dark - unknown roadway lighting2 (0.7%)

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

Road Surface

Dry193 (68.0%)
↑1.6%prior 190
Wet32 (11.3%)
↑23.1%prior 26
Snow22 (7.7%)
↓-12.0%prior 25
Ice/frost21 (7.4%)
↓-25.0%prior 28
Gravel15 (5.3%)
↓-16.7%prior 18
Slush1 (0.4%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes were consistent, with Ford (108 vehicles) and Chevrolet (83 vehicles) being the most common in 2022, similar to 2021 (85 and 79 vehicles, respectively). Analysis of persons involved shows a significant increase in the 26-34 age group, which grew by 33.7% from 92 individuals in 2021 to 123 in 2022. The 16-20, 35-44, 55-64, and 65+ age groups also saw increases in the number of people involved in crashes.

Top Vehicle Makes (541 vehicles)

1
FORD108 (20%)
↑27.1%prior 85
2
CHEV51 (9.4%)
↑21.4%prior 42
3
CHEVROLET32 (5.9%)
↓-13.5%prior 37
4
FREIGHTLINER22 (4.1%)
↑15.8%prior 19
5
JEEP21 (3.9%)
↑31.3%prior 16
6
TOYT20 (3.7%)
↑11.1%prior 18
7
TOYOTA18 (3.3%)
↑12.5%prior 16
8
DODG16 (3%)
↑33.3%prior 12
9
NR16 (3%)
↑23.1%prior 13
10
GMC15 (2.8%)
↑36.4%prior 11

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

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

Sex Distribution (504 persons with recorded sex)

Male339 (67.3%)
↑18.5%prior 286
Female165 (32.7%)
↑13.8%prior 145

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: 372
  • Total persons involved: 762
  • Total vehicles involved: 541

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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