Yearly Traffic Safety Analysis

314 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Tama County, total traffic crashes remained nearly stable, increasing slightly from 311 in 2021 to 314 in 2022, a change of less than 1%. While total crashes were steady and fatalities decreased from 3 to 2, the most notable shift was a significant 34.2% increase in the number of people injured, which rose from 79 to 106 year-over-year.

314

▲ 1.0%was 311

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

106

▲ 34.2%was 79

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

Overall crash volume in Tama County was stable between 2021 and 2022, with only three additional crashes recorded in the current period. However, this stability in total numbers masks a concerning trend in crash outcomes, as the number of individuals injured in these incidents rose by over a third. Fatalities decreased slightly from 3 in the prior year to 2 in the current year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 0→%

104

Motorists Injured

Prior: 79↑31.6%

1

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 timing of crashes shifted between the two periods. The peak day for crashes moved from Monday (54 crashes) in 2021 to Thursday (62 crashes) in 2022. Similarly, the peak hour for collisions shifted from the 5 p.m. evening commute hour in the prior year (32 crashes) to the 9 p.m. hour in the current year (26 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

While the number of fatal crashes decreased from 3 in 2021 to 2 in 2022, crashes resulting in injury became more frequent. The count of minor injury crashes increased from 27 to 37, and possible injury crashes rose from 28 to 30. The number of serious injury crashes remained unchanged at 13 for both years.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
↓-33.3%prior 3
Serious Injury13serious injury crashes4.1%
→0.0%prior 13
Minor Injury37minor injury crashes11.8%
↑37.0%prior 27
Possible Injury30possible injury crashes9.6%
↑7.1%prior 28
No Injury232no injury crashes73.9%
↓-3.3%prior 240

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, with counts increasing from 129 to 135. The second-ranked factor in 2021, 'Lost Control' (23 crashes), saw its count decrease to 18 in 2022. A notable increase was observed in crashes attributed to 'Driver Distraction: Other interior distraction', which more than doubled in count from 6 to 14. 'FTYROW: From yield sign' also saw a significant increase, rising from 3 crashes in 2021 to 9 in 2022.

Officer-Reported Primary Contributing Cause

Animal135 (43%)↑4.7%prior 129
Lost Control18 (5.7%)↓-21.7%prior 23
Driver Distraction: Other interior distraction14 (4.5%)↑133.3%prior 6
Driving too fast for conditions12 (3.8%)↑50.0%prior 8
Ran off road - left12 (3.8%)↑71.4%prior 7
FTYROW: From stop sign11 (3.5%)↓-26.7%prior 15
Ran off road - straight11 (3.5%)↓-45.0%prior 20
Other (explain in narrative): Other11 (3.5%)→0.0%prior 11
Operating vehicle in an reckless, erratic, careless, negligent manner9 (2.9%)↑28.6%prior 7
FTYROW: From yield sign9 (2.9%)

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

Road & Environmental Conditions

Crashes under clear weather and daylight conditions remained the most common and were numerically stable across both years. However, there was a notable increase in crashes occurring on adverse road surfaces. Collisions on roads with ice or frost nearly doubled from 8 to 15, and crashes on gravel roads also increased significantly from 7 to 15 year-over-year. Crashes attributed to freezing rain or drizzle, which were not recorded in 2021, accounted for 10 incidents in 2022.

Weather

Clear141 (70.1%)
↓-0.7%prior 142
Cloudy30 (14.9%)
↓-30.2%prior 43
Freezing rain/drizzle10 (5.0%)
Rain6 (3.0%)
Snow6 (3.0%)
Severe Winds3 (1.5%)
Fog, smoke, smog3 (1.5%)
Other (explain in narrative)1 (0.5%)
Blowing Snow1 (0.5%)

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

Lighting

Daylight127 (63.5%)
→0.0%prior 127
Dark - roadway not lighted39 (19.5%)
↓-20.4%prior 49
Dark - roadway lighted13 (6.5%)
↑30.0%prior 10
Dusk10 (5.0%)
↑100.0%prior 5
Dawn8 (4.0%)
↓-11.1%prior 9
Dark - unknown roadway lighting3 (1.5%)

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

Road Surface

Dry147 (72.8%)
↓-10.9%prior 165
Ice/frost15 (7.4%)
↑87.5%prior 8
Gravel15 (7.4%)
↑114.3%prior 7
Wet13 (6.4%)
↓-13.3%prior 15
Snow11 (5.4%)
↑57.1%prior 7
Mud, dirt1 (0.5%)

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 remained broadly consistent, with Chevrolet and Ford products being the most common in both years. Analysis of persons involved shows an increase in crash involvement across most age demographics, corresponding with the rise in total persons from 552 to 663. The most significant percentage increase was in the 65+ age group, where the number of individuals involved in crashes grew from 58 to 78.

Top Vehicle Makes (421 vehicles)

1
CHEV80 (19%)
↑1.3%prior 79
2
FORD76 (18.1%)
↑10.1%prior 69
3
TOYT22 (5.2%)
↑15.8%prior 19
4
CHEVROLET17 (4%)
↓-48.5%prior 33
5
JEEP16 (3.8%)
↑33.3%prior 12
6
DODG15 (3.6%)
↓-28.6%prior 21
7
HOND12 (2.9%)
↑20.0%prior 10
8
KIA12 (2.9%)
↑140.0%prior 5
9
GMC12 (2.9%)
↓-14.3%prior 14
10
BUIC11 (2.6%)
↑22.2%prior 9

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

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

Sex Distribution (389 persons with recorded sex)

Male245 (63.0%)
↑27.6%prior 192
Female144 (37.0%)
↑17.1%prior 123

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: 314
  • Total persons involved: 663
  • Total vehicles involved: 421

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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