Yearly Traffic Safety Analysis

96 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In 2023, Worth County recorded 96 total crashes, a decrease from 109 crashes in 2022, representing an 11.9% reduction. While total crashes declined, the number of injuries rose from 27 to 39. A notable improvement was the complete elimination of traffic fatalities, which dropped from two in the prior year to zero in the current period.

96

▼ -11.9%was 109

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

39

▲ 44.4%was 27

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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Worth County showed a downward trend, decreasing by 11.9% from 109 incidents in 2022 to 96 in 2023. Despite the drop in total collisions, the number of persons injured increased by 44.4%, rising from 27 to 39. Conversely, traffic fatalities were eliminated, falling from two in the prior year to zero.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 2↓-100.0%

39

Motorists Injured

Prior: 26↑50.0%

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 temporal patterns of crashes showed some shifts between the two periods. While Friday remained the peak day for crashes in both 2023 (18 crashes) and 2022 (20 crashes), the peak hour moved earlier in the day. In 2023, the highest frequency of crashes occurred at 5 p.m. with 8 incidents, a shift from the 9 p.m. peak (10 incidents) observed in the prior year.

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 outcomes improved regarding fatalities, with zero fatal crashes in 2023 compared to two in 2022. However, the overall proportion of crashes involving an injury increased. The share of minor injury crashes rose significantly from 5.5% of total crashes in 2022 to 16.7% in 2023, with the count of such crashes increasing from 6 to 16. The number of serious injury crashes was unchanged at three for both years.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes3.1%
→0.0%prior 3
Minor Injury16minor injury crashes16.7%
↑166.7%prior 6
Possible Injury6possible injury crashes6.3%
↓-40.0%prior 10
No Injury71no injury crashes74%
↓-19.3%prior 88

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 involving an animal remained the leading contributing factor in both periods, increasing slightly from 38 crashes in 2022 to 40 in 2023. "Lost Control" was the second-most cited factor in both years, though its count decreased by 38.5% from 13 incidents to 8. Crashes attributed to "Followed too close" and "FTYROW: From stop sign" both saw an increase in count, rising from 4 to 6 incidents each.

Officer-Reported Primary Contributing Cause

Animal40 (41.7%)↑5.3%prior 38
Lost Control8 (8.3%)↓-38.5%prior 13
Ran off road - straight7 (7.3%)↑40.0%prior 5
FTYROW: From stop sign6 (6.3%)
Followed too close6 (6.3%)
Driving too fast for conditions4 (4.2%)↓-33.3%prior 6
Driver Distraction: Inattentive/lost in thought3 (3.1%)
Other (explain in narrative): Other3 (3.1%)↓-62.5%prior 8
Passing: Other passing (explain in narrative)3 (3.1%)
Ran off road - left3 (3.1%)↓-50.0%prior 6

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

Road & Environmental Conditions

Year-over-year data shows a notable shift in crashes related to road surface conditions. Collisions on icy or frosty roads decreased from 13 to 4, and those on snow-covered roads fell from 10 to 3. Correspondingly, crashes on dry roads remained the most common scenario, accounting for 49 incidents in 2023 compared to 51 in 2022. Regarding lighting, crashes in daylight decreased from 42 to 37, while those on dark, unlit roadways increased slightly from 23 to 26.

Weather

Clear50 (75.8%)
↑16.3%prior 43
Cloudy7 (10.6%)
↓-46.2%prior 13
Snow4 (6.1%)
Rain2 (3.0%)
Freezing rain/drizzle1 (1.5%)
↓-90.0%prior 10
Severe Winds1 (1.5%)
Blowing Snow1 (1.5%)

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

Lighting

Daylight37 (56.1%)
↓-11.9%prior 42
Dark - roadway not lighted26 (39.4%)
↑13.0%prior 23
Dusk2 (3.0%)
Dawn1 (1.5%)
↓-80.0%prior 5

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

Road Surface

Dry49 (74.2%)
↓-3.9%prior 51
Gravel5 (7.6%)
Ice/frost4 (6.1%)
↓-69.2%prior 13
Snow3 (4.5%)
↓-70.0%prior 10
Wet3 (4.5%)
Mud, dirt1 (1.5%)
Slush1 (1.5%)

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 remained consistent, with Chevrolet and Ford vehicles accounting for the highest volumes in both 2023 and 2022. Analysis of the age of persons involved in crashes reveals some demographic shifts. The 35-44 age group continued to be the most frequently involved, with its count rising from 41 to 45. In contrast, the number of persons in the 21-25 age group involved in crashes decreased significantly, from 34 in 2022 to 18 in 2023.

Top Vehicle Makes (129 vehicles)

1
FORD19 (14.7%)
↑18.8%prior 16
2
CHEV19 (14.7%)
↑11.8%prior 17
3
CHEVROLET9 (7%)
↓-25.0%prior 12
4
HONDA8 (6.2%)
↑14.3%prior 7
5
NISS7 (5.4%)
↑40.0%prior 5
6
GMC6 (4.7%)
↑20.0%prior 5
7
TOYOTA4 (3.1%)
8
TOYT4 (3.1%)
9
JEEP4 (3.1%)
↓-42.9%prior 7
10
TOYO4 (3.1%)
↓-55.6%prior 9

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

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

Sex Distribution (127 persons with recorded sex)

Male93 (73.2%)
↑2.2%prior 91
Female34 (26.8%)
↓-38.2%prior 55

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: 96
  • Total persons involved: 203
  • Total vehicles involved: 129

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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