Yearly Traffic Safety Analysis

231 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Jones County, total traffic crashes decreased by 9.1%, from 254 in 2022 to 231 in 2023. While the total number of fatalities remained unchanged at 3 for both years, the number of fatal crashes declined from 3 to 2. A notable shift occurred in crashes resulting in serious injuries, which doubled in number from 4 in 2022 to 8 in 2023, even as the overall crash volume fell.

231

▼ -9.1%was 254

Total Crash Events

3

Persons Killed

66

▲ 1.5%was 65

Persons Injured

2

▼ -33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (3) 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

Traffic crashes in Jones County showed a downward trend, with total incidents falling by 9.1% from 254 in 2022 to 231 in 2023. Despite this overall decrease, the number of people injured saw a slight increase from 65 to 66. The number of fatalities remained stable at 3 persons killed in both periods.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 3→0.0%

1

Cyclists Injured

Prior: 0→%

65

Motorists Injured

Prior: 63↑3.2%

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 shifted year-over-year. The peak day for crashes moved from Tuesday in 2022 (46 crashes) to Thursday in 2023 (46 crashes). The morning rush hour peak also shifted, with the busiest hour for crashes moving from 6 a.m. in 2022 (20 crashes) to 7 a.m. in 2023 (18 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

The fatal crash rate decreased from 1.18% in 2022 to 0.87% in 2023, with fatal crashes dropping from 3 to 2. Conversely, crashes resulting in serious injuries doubled, increasing from 4 incidents (1.6% of all crashes) in 2022 to 8 incidents (3.5% of all crashes) in 2023. The proportion of crashes with no reported injuries remained the largest category and was stable at 78.0% in 2022 and 77.1% in 2023.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.9%
↓-33.3%prior 3
Serious Injury8serious injury crashes3.5%
↑100.0%prior 4
Minor Injury21minor injury crashes9.1%
↓-19.2%prior 26
Possible Injury22possible injury crashes9.5%
↓-4.3%prior 23
No Injury178no injury crashes77.1%
↓-10.1%prior 198

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, although the count of these incidents decreased from 110 in 2022 to 92 in 2023. Crashes attributed to 'Lost Control' also declined from 25 to 18. In contrast, incidents related to stop sign violations increased; crashes involving 'Failure to Yield Right of Way from a stop sign' rose from 6 to 15, and 'Ran Stop Sign' crashes increased from 5 to 9.

Officer-Reported Primary Contributing Cause

Animal92 (39.8%)↓-16.4%prior 110
Lost Control18 (7.8%)↓-28.0%prior 25
FTYROW: From stop sign15 (6.5%)↑150.0%prior 6
Driving too fast for conditions10 (4.3%)↓-9.1%prior 11
Ran Stop Sign9 (3.9%)↑80.0%prior 5
Followed too close8 (3.5%)↑33.3%prior 6
Improper Backing6 (2.6%)
Other (explain in narrative): Other6 (2.6%)↓-40.0%prior 10
Ran off road - straight6 (2.6%)↓-14.3%prior 7
Ran off road - left5 (2.2%)↓-44.4%prior 9

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 on dry roads increased from 108 in 2022 to 120 in 2023, while crashes on adverse surfaces (wet, ice, or snow) collectively dropped from 48 to 23. A similar trend was observed in weather conditions, with crashes in clear weather increasing from 92 to 104, while those in cloudy, snow, or rainy conditions decreased. The number of crashes in daylight saw a minor reduction from 114 to 108.

Weather

Clear104 (67.5%)
↑13.0%prior 92
Cloudy25 (16.2%)
↓-26.5%prior 34
Snow9 (5.8%)
↓-30.8%prior 13
Fog, smoke, smog8 (5.2%)
Freezing rain/drizzle4 (2.6%)
Rain4 (2.6%)
↓-55.6%prior 9

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

Lighting

Daylight108 (68.8%)
↓-5.3%prior 114
Dark - roadway not lighted32 (20.4%)
↑14.3%prior 28
Dark - roadway lighted12 (7.6%)
↑20.0%prior 10
Dusk4 (2.5%)
Dawn1 (0.6%)

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

Road Surface

Dry120 (77.9%)
↑11.1%prior 108
Wet9 (5.8%)
↓-52.6%prior 19
Ice/frost7 (4.5%)
↓-53.3%prior 15
Snow7 (4.5%)
↓-50.0%prior 14
Gravel6 (3.9%)
Other (explain in narrative)2 (1.3%)
Slush2 (1.3%)
Oil1 (0.6%)

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

Vehicles & Demographics

The demographic profile of persons involved in crashes shifted, with the 16-20 age group seeing an increase from 44 individuals in 2022 to 69 in 2023. Conversely, involvement for the 35-44 age group decreased from 97 to 71. Among vehicle makes, Ford's involvement increased from 68 to 75 vehicles, while the number of Chevrolet vehicles involved in crashes declined from 73 to 62.

Top Vehicle Makes (332 vehicles)

1
FORD75 (22.6%)
↑10.3%prior 68
2
CHEV47 (14.2%)
↓-4.1%prior 49
3
CHEVROLET15 (4.5%)
↓-37.5%prior 24
4
DODG15 (4.5%)
↓-11.8%prior 17
5
GMC13 (3.9%)
↓-27.8%prior 18
6
NISS12 (3.6%)
↑33.3%prior 9
7
TOYO10 (3%)
↓-23.1%prior 13
8
JEEP10 (3%)
↑11.1%prior 9
9
HYUN8 (2.4%)
10
SUBA7 (2.1%)
→0.0%prior 7

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

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

Sex Distribution (312 persons with recorded sex)

Male172 (55.1%)
↓-12.7%prior 197
Female140 (44.9%)
↑11.1%prior 126

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: 231
  • Total persons involved: 492
  • Total vehicles involved: 332

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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