Yearly Traffic Safety Analysis

575 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Muscatine County, total traffic crashes increased by 4.9%, rising from 548 incidents in 2021 to 575 in 2022. Despite the overall increase in collisions, the outcomes were less severe, with total injuries declining by 16.2% from 241 to 202. The most notable shift was a 33.3% reduction in crashes involving driving under the influence (DUI), which fell from 30 to 20 incidents year-over-year.

575

▲ 4.9%was 548

Total Crash Events

6

▼ -14.3%was 7

Persons Killed

202

▼ -16.2%was 241

Persons Injured

5

▼ -28.6%was 7

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 Muscatine County showed a slight upward trend, increasing by 27 incidents, or 4.9%, from 2021 to 2022. However, this increase in crash volume was accompanied by a positive trend in safety outcomes. The number of people injured in these crashes decreased by 16.2% from 241 to 202, and fatalities dropped from 7 to 6.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 7↓-14.3%

9

Pedestrians Injured

Prior: 4↑125.0%

4

Cyclists Injured

Prior: 1↑300.0%

189

Motorists Injured

Prior: 235↓-19.6%

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 changes between the two years. Thursday remained the day with the most crashes in both 2021 (100 crashes) and 2022 (113 crashes). However, the single hour with the highest crash frequency shifted earlier in the day, from 6 p.m. in 2021 (43 crashes) to 3 p.m. in 2022 (43 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

The overall severity of crashes in Muscatine County lessened from 2021 to 2022. The fatal crash rate fell from 1.28% to 0.87%, corresponding to a drop from 7 to 5 fatal crashes. The proportion of crashes resulting in any injury decreased from a combined 32.9% in 2021 to 26.9% in 2022. Consequently, the share of property-damage-only crashes increased from 65.9% of all incidents in 2021 to 72.2% in 2022.

Severity is per crash event (most severe injury). 5 fatal crash events resulted in 6 persons killed.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.9%
↓-28.6%prior 7
Serious Injury19serious injury crashes3.3%
→0.0%prior 19
Minor Injury64minor injury crashes11.1%
↓-8.6%prior 70
Possible Injury72possible injury crashes12.5%
↓-20.9%prior 91
No Injury415no injury crashes72.2%
↑15.0%prior 361

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 leading contributing factor in both periods, with the count of such incidents rising 20% from 135 in 2021 to 162 in 2022. Crashes attributed to 'Failure to Yield Right of Way from a stop sign' decreased from 40 to 32 incidents. Conversely, crashes involving 'Lost Control' increased from 24 to 32, and 'Ran off road - left' rose from 30 to 34 incidents.

Officer-Reported Primary Contributing Cause

Animal162 (28.2%)↑20.0%prior 135
Other (explain in narrative): Other54 (9.4%)↑31.7%prior 41
Ran off road - left34 (5.9%)↑13.3%prior 30
FTYROW: From stop sign32 (5.6%)↓-20.0%prior 40
Lost Control32 (5.6%)↑33.3%prior 24
FTYROW: Making left turn28 (4.9%)↑7.7%prior 26
Followed too close24 (4.2%)↓-27.3%prior 33
Ran Stop Sign23 (4%)↑27.8%prior 18
Operating vehicle in an reckless, erratic, careless, negligent manner18 (3.1%)↓-14.3%prior 21
Ran off road - straight17 (3%)↑54.5%prior 11

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 2021 and 2022 occurred in clear weather on dry roads. A significant shift was observed in crashes on icy or frosty roads, which plummeted from 34 incidents in 2021 to 12 in 2022. Crashes during daylight hours decreased slightly from 296 to 286, while those occurring in the dark on lighted roadways increased from 56 to 71.

Weather

Clear309 (71.0%)
↑1.3%prior 305
Cloudy63 (14.5%)
↓-1.6%prior 64
Rain23 (5.3%)
↑53.3%prior 15
Snow18 (4.1%)
↑28.6%prior 14
Other (explain in narrative)9 (2.1%)
↑80.0%prior 5
Blowing Snow7 (1.6%)
Freezing rain/drizzle5 (1.1%)
↓-58.3%prior 12
Severe Winds1 (0.2%)

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

Lighting

Daylight286 (65.4%)
↓-3.4%prior 296
Dark - roadway lighted71 (16.2%)
↑26.8%prior 56
Dark - roadway not lighted54 (12.4%)
↑8.0%prior 50
Dusk15 (3.4%)
↑36.4%prior 11
Dawn8 (1.8%)
↓-27.3%prior 11
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

Dry348 (79.6%)
↑11.2%prior 313
Wet45 (10.3%)
→0.0%prior 45
Snow23 (5.3%)
↑9.5%prior 21
Ice/frost12 (2.7%)
↓-64.7%prior 34
Slush5 (1.1%)
→0.0%prior 5
Gravel3 (0.7%)
↓-57.1%prior 7
Mud, dirt1 (0.2%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved in crashes in both years; Fords increased from 139 to 148, while Chevrolets remained nearly static (177 to 179). Analysis of persons involved in crashes reveals a shift in age demographics. The number of individuals in the 16-20 age group involved in crashes decreased from 156 to 123. In contrast, involvement increased among older drivers, with the 55-64 age group growing from 123 to 168 people and the 65+ group increasing from 118 to 160 people.

Top Vehicle Makes (887 vehicles)

1
FORD148 (16.7%)
↑6.5%prior 139
2
CHEV113 (12.7%)
↑5.6%prior 107
3
CHEVROLET66 (7.4%)
↓-5.7%prior 70
4
TOYT56 (6.3%)
↓-3.4%prior 58
5
DODG45 (5.1%)
↑36.4%prior 33
6
GMC37 (4.2%)
↑15.6%prior 32
7
NISS36 (4.1%)
↑89.5%prior 19
8
HOND30 (3.4%)
↑3.4%prior 29
9
JEEP29 (3.3%)
↓-14.7%prior 34
10
TOYOTA22 (2.5%)
↓-37.1%prior 35

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

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

Sex Distribution (798 persons with recorded sex)

Male491 (61.5%)
↑29.2%prior 380
Female307 (38.5%)
↑8.9%prior 282

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: 575
  • Total persons involved: 1,289
  • Total vehicles involved: 887

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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