Yearly Traffic Safety Analysis

165 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Franklin County recorded 165 total crashes, a decrease of 8.8% from the 181 crashes reported in 2021. While total injuries saw a slight increase from 39 to 44, the most significant year-over-year change was the reduction in traffic fatalities, which dropped from two in 2021 to zero in 2022.

165

▼ -8.8%was 181

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

44

▲ 12.8%was 39

Persons Injured

0

▼ -100.0%was 2

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

The overall trend in traffic crashes in Franklin County showed a decrease from 2021 to 2022. Total collisions fell by 8.8%, from 181 to 165. Despite the drop in total crashes, the number of people injured increased from 39 to 44, while fatalities were eliminated, dropping from two to zero.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 2↓-100.0%

44

Motorists Injured

Prior: 39↑12.8%

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 in Franklin County shifted between 2021 and 2022. The peak day for collisions moved from Saturday (34 crashes) in the prior year to Thursday (32 crashes) in the current year. Similarly, the peak hour for crashes changed from 6 p.m. (16 crashes) in 2021 to 1 p.m. (14 crashes) in 2022, indicating a shift from evening to early afternoon.

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 outcomes improved from 2021 to 2022, with fatal crashes dropping from two to zero. However, the number of crashes resulting in injuries increased. Serious injury crashes rose from 3 to 5, and minor injury crashes increased from 10 to 18. Consequently, the proportion of all crashes that resulted in no injuries decreased from 84.0% in 2021 to 78.2% in 2022.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes3%
↑66.7%prior 3
Minor Injury18minor injury crashes10.9%
↑80.0%prior 10
Possible Injury13possible injury crashes7.9%
↓-7.1%prior 14
No Injury129no injury crashes78.2%
↓-15.1%prior 152

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 top contributing factor in both periods, though the count of these incidents decreased by 27.1% from 59 in 2021 to 43 in 2022. The second most common factor in 2021, 'Lost Control,' saw its count fall by 54.5% from 22 crashes to 10. Conversely, crashes attributed to 'Driving too fast for conditions' increased in count from 6 to 11. 'Failure to yield from a stop sign' crashes also saw a notable drop from 9 incidents in 2021 to just 2 in 2022.

Officer-Reported Primary Contributing Cause

Animal43 (26.1%)↓-27.1%prior 59
Other (explain in narrative): Other15 (9.1%)↑66.7%prior 9
Driving too fast for conditions11 (6.7%)↑83.3%prior 6
FTYROW: At uncontrolled intersection10 (6.1%)↑25.0%prior 8
Lost Control10 (6.1%)↓-54.5%prior 22
Ran off road - straight9 (5.5%)↑12.5%prior 8
Ran off road - left8 (4.8%)↓-20.0%prior 10
Improper Backing6 (3.6%)
Driver Distraction: Other interior distraction5 (3%)
FTYROW: From driveway4 (2.4%)

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 distribution of crashes across weather and road surface conditions remained relatively stable year-over-year. Crashes on dry roads (91 vs. 90) and in clear weather (92 vs. 90) were nearly identical. However, there was a noticeable shift in lighting conditions; the number of crashes occurring in daylight increased from 78 in 2021 to 93 in 2022, while crashes in dark conditions decreased from 50 to 36.

Weather

Clear92 (69.7%)
↑2.2%prior 90
Cloudy21 (15.9%)
↑16.7%prior 18
Blowing Snow7 (5.3%)
Severe Winds4 (3.0%)
Snow4 (3.0%)
↓-20.0%prior 5
Rain2 (1.5%)
Freezing rain/drizzle1 (0.8%)
↓-87.5%prior 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

Daylight93 (69.9%)
↑19.2%prior 78
Dark - roadway not lighted25 (18.8%)
↓-35.9%prior 39
Dark - roadway lighted10 (7.5%)
↓-9.1%prior 11
Dawn3 (2.3%)
Dusk1 (0.8%)
Dark - unknown roadway lighting1 (0.8%)

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

Road Surface

Dry91 (68.4%)
↑1.1%prior 90
Snow20 (15.0%)
↑81.8%prior 11
Ice/frost12 (9.0%)
↓-7.7%prior 13
Wet6 (4.5%)
↓-45.5%prior 11
Gravel3 (2.3%)
↓-62.5%prior 8
Other (explain in narrative)1 (0.8%)

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 consistent, with Ford and Chevrolet representing the most common brands in both 2021 and 2022. The number of Chevrolet vehicles involved decreased from 62 to 50, while Ford vehicles increased from 40 to 44. Regarding the age of persons involved, there was an increase in the 21-25 age group (from 36 to 47 individuals) and the 45-54 age group (from 39 to 49 individuals) compared to the prior year.

Top Vehicle Makes (250 vehicles)

1
FORD44 (17.6%)
↑10.0%prior 40
2
CHEV35 (14%)
↓-10.3%prior 39
3
CHEVROLET15 (6%)
↓-34.8%prior 23
4
DODGE12 (4.8%)
5
JEEP10 (4%)
↑66.7%prior 6
6
FREIGHTLINER9 (3.6%)
↑12.5%prior 8
7
GMC9 (3.6%)
→0.0%prior 9
8
RAM7 (2.8%)
9
TOYOTA7 (2.8%)
↓-12.5%prior 8
10
CHRY7 (2.8%)

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

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

Sex Distribution (229 persons with recorded sex)

Male138 (60.3%)
↑0.7%prior 137
Female91 (39.7%)
↑30.0%prior 70

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: 165
  • Total persons involved: 347
  • Total vehicles involved: 250

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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