Yearly Traffic Safety Analysis

56 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In 2024, Ringgold County recorded 56 total crashes, the same number as in 2023. While the overall crash volume remained stable, representing a 0% change, the number of fatalities decreased by 50%, from 2 in the prior period to 1 in the current period. Total injuries also saw a slight decrease from 21 to 19.

56

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

19

▼ -9.5%was 21

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall crash trend in Ringgold County was stable year-over-year, with a total of 56 crashes reported in both 2024 and 2023. This represents a 0% change in crash volume. However, the severity of these incidents trended downward, with total fatalities dropping from 2 to 1 and total injuries decreasing from 21 to 19.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

19

Motorists Injured

Prior: 21↓-9.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal patterns of crashes shifted between the two periods. The peak day for crashes moved from Saturday (13 crashes) in 2023 to Thursday (10 crashes) in 2024. Similarly, the peak hour shifted slightly earlier, from 10 p.m. in the prior period (6 crashes) to 9 p.m. in the current period (5 crashes). Crashes on Fridays and Saturdays decreased notably, while incidents on Tuesdays and Thursdays increased.

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes remained constant at 1 in both 2024 and 2023, the total number of people killed decreased from 2 to 1. The distribution of injury severity shifted, with crashes resulting in 'Possible Injury' decreasing from 9 to 4. Conversely, crashes involving 'Minor Injury' increased from 5 in the prior period to 8 in the current period. The proportion of crashes with no injuries increased slightly from 66.1% to 69.6%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.8%
→0.0%prior 1
Serious Injury4serious injury crashes7.1%
→0.0%prior 4
Minor Injury8minor injury crashes14.3%
↑60.0%prior 5
Possible Injury4possible injury crashes7.1%
↓-55.6%prior 9
No Injury39no injury crashes69.6%
↑5.4%prior 37

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an 'Animal' remained the top contributing factor in both periods, increasing slightly from 12 crashes in 2023 to 13 in 2024. The count for crashes attributed to 'Lost Control' doubled, rising from 2 to 4 incidents, a 100% increase in count. Similarly, 'Driving too fast for conditions' was cited in 3 crashes in the current period, up from just 1 in the prior period. The top three factors in 2024 were 'Animal' (13 crashes), 'Other' (8 crashes), and 'Ran off road - straight' (6 crashes), maintaining a similar ranking structure to the previous year.

Officer-Reported Primary Contributing Cause

Animal13 (23.2%)↑8.3%prior 12
Other (explain in narrative): Other8 (14.3%)↓-11.1%prior 9
Ran off road - straight6 (10.7%)↑20.0%prior 5
Followed too close4 (7.1%)
Lost Control4 (7.1%)
Ran off road - left3 (5.4%)
Driving too fast for conditions3 (5.4%)
Driver Distraction: Other interior distraction3 (5.4%)
FTYROW: From stop sign2 (3.6%)
Ran Stop Sign1 (1.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring on adverse road surfaces increased year-over-year. In 2024, 12 crashes occurred on surfaces described as snow, wet, ice/frost, slush, or gravel, compared to 8 such crashes in 2023. Crashes in dark, unlighted conditions decreased, accounting for 10 incidents in the current period versus 16 in the prior period. The majority of crashes in both years occurred in clear weather, though the share of crashes on dry surfaces fell from 80.4% in 2023 to 66.1% in 2024.

Weather

Clear39 (79.6%)
↓-7.1%prior 42
Cloudy4 (8.2%)
Snow4 (8.2%)
Rain2 (4.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight30 (60.0%)
↓-6.3%prior 32
Dark - roadway not lighted10 (20.0%)
↓-37.5%prior 16
Dark - roadway lighted4 (8.0%)
Dawn3 (6.0%)
Dusk2 (4.0%)
Dark - unknown roadway lighting1 (2.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry37 (75.5%)
↓-17.8%prior 45
Snow4 (8.2%)
Wet3 (6.1%)
Ice/frost2 (4.1%)
Gravel2 (4.1%)
Slush1 (2.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes showed notable shifts. Ford became the most common make in 2024 with 21 vehicles, up from 13 in 2023. Conversely, Chevrolet-branded vehicles, the top make in the prior year with 22 vehicles, decreased to 18. Among persons involved, there was a significant increase in the 26-34 age group, which grew from 3 individuals in 2023 to 14 in 2024. In contrast, the number of persons aged 65 and older involved in crashes dropped sharply from 19 to 7.

Top Vehicle Makes (81 vehicles)

1
FORD21 (25.9%)
↑61.5%prior 13
2
CHEV11 (13.6%)
↓-21.4%prior 14
3
CHEVROLET7 (8.6%)
↓-12.5%prior 8
4
GMC6 (7.4%)
→0.0%prior 6
5
JEEP5 (6.2%)
6
DODG3 (3.7%)
↓-40.0%prior 5
7
DODGE3 (3.7%)
8
NISS2 (2.5%)
9
CHRY2 (2.5%)
10
INTERNATIONA1 (1.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (49 persons with recorded sex)

Male27 (55.1%)
↓-44.9%prior 49
Female22 (44.9%)
↓-24.1%prior 29

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 56
  • Total persons involved: 84
  • Total vehicles involved: 81

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: 2024." Published September 9, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2024-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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