Yearly Traffic Safety Analysis

146 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Greene County, total traffic crashes remained relatively stable, with 146 incidents in 2023 compared to 148 in 2022, a decrease of 1.4%. While the overall crash volume was steady, the most notable year-over-year shift was a significant increase in crash severity. The county recorded five fatalities in 2023, a stark contrast to zero fatalities in the prior year.

146

▼ -1.4%was 148

Total Crash Events

5

Persons Killed

58

▲ 26.1%was 46

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

While the total number of crashes in Greene County saw a minor decrease from 148 in 2022 to 146 in 2023, the outcomes of these incidents worsened. Total injuries rose by 26.1%, from 46 to 58 people injured. Most significantly, the number of fatalities increased from zero in 2022 to five in 2023.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 0→%

2

Cyclists Injured

Prior: 0→%

56

Motorists Injured

Prior: 46↑21.7%

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 shifted between the two periods. In 2023, the peak day for crashes was Wednesday with 26 incidents, a change from Friday, which saw 31 crashes in 2022. The busiest time of day also shifted slightly later, with the peak hour for collisions moving from 5 p.m. in 2022 (18 crashes) to 6 p.m. in 2023 (16 crashes).

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 increased markedly in 2023 compared to 2022. Four fatal crashes occurred in 2023, resulting in five deaths, whereas there were no fatal crashes in the prior year, raising the fatal crash rate from 0% to 2.7%. The count of serious injury crashes also rose from 6 to 7. Consequently, the proportion of crashes resulting in no injury decreased from 73.0% in 2022 to 71.2% in 2023.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes2.7%
Serious Injury7serious injury crashes4.8%
↑16.7%prior 6
Minor Injury18minor injury crashes12.3%
↑12.5%prior 16
Possible Injury13possible injury crashes8.9%
↓-27.8%prior 18
No Injury104no injury crashes71.2%
↓-3.7%prior 108

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 remained the leading contributing factor in both periods, accounting for 48 crashes in 2023 and 49 in 2022. The count of crashes attributed to 'Operating vehicle in a reckless, erratic, careless, negligent manner' more than doubled, increasing from 3 in 2022 to 8 in 2023. Conversely, crashes linked to 'Driver Distraction: Other interior distraction' saw a significant decrease, falling from 8 incidents in 2022 to 3 in 2023.

Officer-Reported Primary Contributing Cause

Animal48 (32.9%)↓-2.0%prior 49
Ran off road - left9 (6.2%)↑28.6%prior 7
FTYROW: At uncontrolled intersection8 (5.5%)↓-11.1%prior 9
Operating vehicle in an reckless, erratic, careless, negligent manner8 (5.5%)
Lost Control8 (5.5%)↓-11.1%prior 9
Ran Stop Sign7 (4.8%)↓-22.2%prior 9
FTYROW: From stop sign6 (4.1%)↓-25.0%prior 8
Ran off road - straight6 (4.1%)
Driving too fast for conditions5 (3.4%)
Followed too close4 (2.7%)↓-42.9%prior 7

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

Road & Environmental Conditions

The majority of crashes in both 2023 and 2022 occurred in clear weather and on dry roads, with these conditions remaining stable as the predominant environment for collisions. In 2023, crashes during daylight hours increased to 75 from 67 in the prior year. Correspondingly, incidents taking place on dark, unlighted roadways decreased from 23 in 2022 to 14 in 2023.

Weather

Clear84 (80.8%)
→0.0%prior 84
Cloudy12 (11.5%)
↓-36.8%prior 19
Snow3 (2.9%)
Freezing rain/drizzle2 (1.9%)
Rain2 (1.9%)
Other (explain in narrative)1 (1.0%)

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

Lighting

Daylight75 (71.4%)
↑11.9%prior 67
Dark - roadway not lighted14 (13.3%)
↓-39.1%prior 23
Dark - roadway lighted8 (7.6%)
↑14.3%prior 7
Dark - unknown roadway lighting4 (3.8%)
Dawn3 (2.9%)
Dusk1 (1.0%)
↓-80.0%prior 5

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

Road Surface

Dry82 (79.6%)
↓-5.7%prior 87
Wet7 (6.8%)
→0.0%prior 7
Snow5 (4.9%)
↓-28.6%prior 7
Gravel5 (4.9%)
→0.0%prior 5
Ice/frost3 (2.9%)
Slush1 (1.0%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes were consistent year-over-year, with Chevrolet (46 vehicles) and Ford (38 vehicles) leading in 2023, similar to their involvement in 2022. A notable shift occurred in the age demographics of persons involved in crashes; the 26-34 age group saw its involvement increase from 45 individuals in 2022 to 62 in 2023. In contrast, the number of individuals in the 16-20 age group involved in crashes decreased from 49 to 31.

Top Vehicle Makes (210 vehicles)

1
CHEV46 (21.9%)
↑2.2%prior 45
2
FORD38 (18.1%)
↓-7.3%prior 41
3
DODG14 (6.7%)
↓-12.5%prior 16
4
JEEP9 (4.3%)
↓-25.0%prior 12
5
TOYO8 (3.8%)
6
NISS8 (3.8%)
7
HOND7 (3.3%)
8
TOYT7 (3.3%)
↓-22.2%prior 9
9
CHEVROLET7 (3.3%)
→0.0%prior 7
10
CHRY6 (2.9%)

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

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

Sex Distribution (201 persons with recorded sex)

Male120 (59.7%)
↑0.8%prior 119
Female81 (40.3%)
↓-8.0%prior 88

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: 146
  • Total persons involved: 323
  • Total vehicles involved: 210

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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