Yearly Traffic Safety Analysis

567 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Muscatine County, total traffic crashes remained stable, decreasing by 1.4% from 575 in 2022 to 567 in 2023. This period saw total fatalities fall from 6 to 4 and injuries from 202 to 195. The most significant year-over-year shift was a 35% increase in crashes involving a driver under the influence (DUI), which rose from 20 incidents in 2022 to 27 in 2023.

567

▼ -1.4%was 575

Total Crash Events

4

▼ -33.3%was 6

Persons Killed

195

▼ -3.5%was 202

Persons Injured

2

▼ -60.0%was 5

Fatal Crash Events

Note: "Persons Killed" (4) 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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends in Muscatine County showed relative stability with a slight decline year-over-year. Total collisions decreased by 1.4%, from 575 in 2022 to 567 in 2023. This trend was mirrored by small decreases in negative outcomes, as fatalities dropped from 6 to 4 and total injuries fell from 202 to 195.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 6↓-33.3%

2

Pedestrians Injured

Prior: 9↓-77.8%

6

Cyclists Injured

Prior: 4↑50.0%

187

Motorists Injured

Prior: 189↓-1.1%

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes was highly consistent between the two periods. Thursday remained the peak day for collisions in both 2023 (100 crashes) and 2022 (113 crashes). The 3 p.m. hour was also the most frequent time for crashes in both years, accounting for 42 incidents in 2023 and 43 in 2022, indicating no significant shift in daily or weekly patterns.

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased in 2023 compared to the prior year. The number of fatal crashes fell from 5 to 2, reducing the fatal crash rate from 0.9% to 0.4% of all incidents. While the overall proportion of crashes resulting in an injury was unchanged at 27.0%, the severity of those injuries lessened, with serious injury crashes declining from 19 to 12 and minor injury crashes falling from 64 to 51.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.4%
↓-60.0%prior 5
Serious Injury12serious injury crashes2.1%
↓-36.8%prior 19
Minor Injury51minor injury crashes9%
↓-20.3%prior 64
Possible Injury90possible injury crashes15.9%
↑25.0%prior 72
No Injury412no injury crashes72.7%
↓-0.7%prior 415

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals were the leading contributing factor in both years, with counts remaining steady at 156 in 2023 versus 162 in 2022. A significant change was observed in crashes attributed to "Followed too close," where the incident count increased by 66.7% from 24 to 40, elevating it to the second-ranked cause in 2023. Incidents involving "Failure to yield from a stop sign" also grew from 32 to 39, while crashes from "Lost Control" decreased from 32 to 27.

Officer-Reported Primary Contributing Cause

Animal156 (27.5%)↓-3.7%prior 162
Followed too close40 (7.1%)↑66.7%prior 24
FTYROW: From stop sign39 (6.9%)↑21.9%prior 32
Other (explain in narrative): Other35 (6.2%)↓-35.2%prior 54
Ran off road - left31 (5.5%)↓-8.8%prior 34
Lost Control27 (4.8%)↓-15.6%prior 32
Ran off road - straight19 (3.4%)↑11.8%prior 17
FTYROW: Making left turn19 (3.4%)↓-32.1%prior 28
Ran Stop Sign18 (3.2%)↓-21.7%prior 23
Driver Distraction: Other interior distraction17 (3%)↑112.5%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in 2023 occurred more frequently under clear conditions compared to the previous year. Incidents on dry roads increased from 348 to 356, and crashes in clear weather rose from 309 to 322. Correspondingly, there was a notable decrease in crashes occurring on adverse road surfaces like wet, snow, or ice, which fell from 85 incidents in 2022 to 63 in 2023. Crashes in daylight also increased from 286 to 305.

Weather

Clear322 (75.2%)
↑4.2%prior 309
Cloudy60 (14.0%)
↓-4.8%prior 63
Snow20 (4.7%)
↑11.1%prior 18
Fog, smoke, smog11 (2.6%)
Rain8 (1.9%)
↓-65.2%prior 23
Other (explain in narrative)4 (0.9%)
↓-55.6%prior 9
Freezing rain/drizzle3 (0.7%)
↓-40.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight305 (70.1%)
↑6.6%prior 286
Dark - roadway lighted57 (13.1%)
↓-19.7%prior 71
Dark - roadway not lighted55 (12.6%)
↑1.9%prior 54
Dusk10 (2.3%)
↓-33.3%prior 15
Dawn5 (1.1%)
↓-37.5%prior 8
Dark - unknown roadway lighting3 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry356 (83.2%)
↑2.3%prior 348
Wet31 (7.2%)
↓-31.1%prior 45
Ice/frost15 (3.5%)
↑25.0%prior 12
Snow15 (3.5%)
↓-34.8%prior 23
Gravel8 (1.9%)
Slush2 (0.5%)
↓-60.0%prior 5
Other (explain in narrative)1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes were consistent, with Ford and Chevrolet vehicles being the most frequent in both periods. An analysis of persons involved in crashes shows a notable demographic shift, as the number of individuals aged 16-20 increased from 123 to 160. Conversely, involvement for the 65+ age group decreased from 160 to 135 persons, and the 21-25 age group saw a decline from 124 to 106 persons.

Top Vehicle Makes (896 vehicles)

1
FORD137 (15.3%)
↓-7.4%prior 148
2
CHEV131 (14.6%)
↑15.9%prior 113
3
TOYT67 (7.5%)
↑19.6%prior 56
4
CHEVROLET57 (6.4%)
↓-13.6%prior 66
5
DODG41 (4.6%)
↓-8.9%prior 45
6
NISS37 (4.1%)
↑2.8%prior 36
7
JEEP37 (4.1%)
↑27.6%prior 29
8
GMC34 (3.8%)
↓-8.1%prior 37
9
HOND32 (3.6%)
↑6.7%prior 30
10
TOYOTA25 (2.8%)
↑13.6%prior 22

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (808 persons with recorded sex)

Male475 (58.8%)
↓-3.3%prior 491
Female333 (41.2%)
↑8.5%prior 307

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 567
  • Total persons involved: 1,281
  • Total vehicles involved: 896

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: 2023." Published September 9, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2023-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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