Yearly Traffic Safety Analysis

669 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Lee County recorded 669 total crashes, representing a 12.4% decrease from the 764 crashes reported in 2021. While overall crashes and injuries declined, the most notable year-over-year shift was a 75% increase in traffic-related fatalities, which rose from 4 in 2021 to 7 in 2022. This divergence highlights a trend of fewer but more severe collisions in the current period.

669

▼ -12.4%was 764

Total Crash Events

7

▲ 75.0%was 4

Persons Killed

143

▼ -19.2%was 177

Persons Injured

7

▲ 75.0%was 4

Fatal Crash Events

Note: "Persons Killed" (7) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) 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 traffic crashes in Lee County decreased by 12.4% from 764 incidents in 2021 to 669 in 2022. This decline was mirrored by a 19.2% drop in total persons injured, from 177 to 143. In contrast to these downward trends, the number of fatalities increased from 4 in the prior period to 7 in the current period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

1

Cyclists Killed

Prior: 0→%

5

Motorists Killed

Prior: 4↑25.0%

2

Pedestrians Injured

Prior: 4↓-50.0%

2

Cyclists Injured

Prior: 8↓-75.0%

139

Motorists Injured

Prior: 165↓-15.8%

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 peak hour for crashes remained consistent year-over-year, with the 5 p.m. hour experiencing the highest volume in both 2022 (55 crashes) and 2021 (51 crashes). However, the peak day for incidents shifted from Friday in 2021, which saw 134 crashes, to Thursday in 2022, which recorded 131 crashes. Crash volumes on Fridays decreased to 115 in the current period.

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 despite a lower overall volume, as the fatal crash rate more than doubled from 0.52% in 2021 to 1.05% in 2022. This reflects an increase from 4 to 7 fatal crashes. The share of crashes resulting in any injury decreased slightly from 19.0% to 17.9%. While the count of serious injury crashes fell from 24 to 16, crashes involving minor injuries rose from 46 to 52.

Outcome by Severity (Crash Events)

Fatal7fatal crashes1%
↑75.0%prior 4
Serious Injury16serious injury crashes2.4%
↓-33.3%prior 24
Minor Injury52minor injury crashes7.8%
↑13.0%prior 46
Possible Injury52possible injury crashes7.8%
↓-30.7%prior 75
No Injury542no injury crashes81%
↓-11.9%prior 615

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 involving an animal remained the top contributing factor in both periods, with the count decreasing from 269 in 2021 to 256 in 2022. A significant change was observed in crashes due to 'Failure to yield right of way from a stop sign,' which fell by 35.7% from 56 to 36 incidents. Conversely, crashes where a driver 'Lost Control' increased by 27.3% in count, rising from 33 in 2021 to 42 in 2022, and moving from the fifth to the third most frequent factor.

Officer-Reported Primary Contributing Cause

Animal256 (38.3%)↓-4.8%prior 269
Other (explain in narrative): Other42 (6.3%)↓-40.0%prior 70
Lost Control42 (6.3%)↑27.3%prior 33
FTYROW: From stop sign36 (5.4%)↓-35.7%prior 56
Driver Distraction: Other interior distraction33 (4.9%)↑10.0%prior 30
Followed too close27 (4%)↓-22.9%prior 35
Ran off road - left26 (3.9%)↓-16.1%prior 31
Ran off road - straight18 (2.7%)↑12.5%prior 16
Ran Stop Sign16 (2.4%)↑14.3%prior 14
FTYROW: Making left turn14 (2.1%)↓-6.7%prior 15

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 environmental conditions remained largely stable between the two periods. The majority of collisions in both 2022 and 2021 occurred in clear weather, during daylight hours, and on dry road surfaces. The absolute number of crashes in dark lighting conditions decreased from 140 to 115, and on non-dry road surfaces from 124 to 114, which aligns with the overall reduction in total crashes. The proportional share of crashes under these conditions did not change significantly.

Weather

Clear295 (65.3%)
↓-16.7%prior 354
Cloudy97 (21.5%)
↓-3.0%prior 100
Rain26 (5.8%)
↑18.2%prior 22
Snow17 (3.8%)
↓-19.0%prior 21
Freezing rain/drizzle10 (2.2%)
Blowing Snow6 (1.3%)
↓-14.3%prior 7
Fog, smoke, smog1 (0.2%)
↓-83.3%prior 6

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

Lighting

Daylight317 (70.0%)
↓-13.2%prior 365
Dark - roadway not lighted85 (18.8%)
↑1.2%prior 84
Dark - roadway lighted27 (6.0%)
↓-50.0%prior 54
Dusk11 (2.4%)
↑37.5%prior 8
Dawn10 (2.2%)
↑25.0%prior 8
Dark - unknown roadway lighting3 (0.7%)

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

Road Surface

Dry339 (74.8%)
↓-14.6%prior 397
Wet43 (9.5%)
↓-17.3%prior 52
Snow28 (6.2%)
↓-9.7%prior 31
Ice/frost20 (4.4%)
↓-4.8%prior 21
Gravel12 (2.6%)
↓-7.7%prior 13
Slush7 (1.5%)
↑40.0%prior 5
Mud, dirt2 (0.4%)
Sand1 (0.2%)
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, including Ford, Chevrolet, and Dodge, remained consistent across both years, with counts for each decreasing in 2022. A notable demographic shift occurred among persons involved in crashes. The 65+ age group became the largest cohort in 2022 with 221 individuals, increasing from 206 in 2021. In contrast, the 35-44 age group, which was the largest in the prior year with 234 individuals, saw its count drop to 196.

Top Vehicle Makes (971 vehicles)

1
FORD166 (17.1%)
↓-15.3%prior 196
2
CHEV135 (13.9%)
↑18.4%prior 114
3
CHEVROLET69 (7.1%)
↓-43.0%prior 121
4
GMC52 (5.4%)
↓-11.9%prior 59
5
DODG47 (4.8%)
↓-7.8%prior 51
6
DODGE35 (3.6%)
↓-30.0%prior 50
7
JEEP30 (3.1%)
↓-6.3%prior 32
8
CHRY29 (3%)
↑3.6%prior 28
9
KIA29 (3%)
↓-31.0%prior 42
10
HOND26 (2.7%)
↓-18.8%prior 32

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

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

Sex Distribution (879 persons with recorded sex)

Male488 (55.5%)
↓-3.4%prior 505
Female391 (44.5%)
↓-3.5%prior 405

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: 669
  • Total persons involved: 1,408
  • Total vehicles involved: 971

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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