Yearly Traffic Safety Analysis

212 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Jefferson County, total traffic crashes increased by approximately 5%, rising from 202 incidents in 2023 to 212 in 2024. While the number of fatalities remained stable at one and total injuries decreased, the most notable year-over-year shift was a sharp increase in the number of crashes resulting in serious injuries, which nearly doubled from 7 to 13 incidents.

212

▲ 5.0%was 202

Total Crash Events

1

Persons Killed

64

▼ -19.0%was 79

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

Overall traffic safety trends in Jefferson County show a slight rise in the total number of crashes, which increased by 5% from 202 in the prior year to 212 in the current year. Despite this increase in collisions, the total number of injuries reported saw a significant 19% decrease, falling from 79 to 64. The number of fatalities was unchanged, with one recorded in both periods.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

3

Pedestrians Injured

Prior: 1↑200.0%

4

Cyclists Injured

Prior: 2↑100.0%

57

Motorists Injured

Prior: 76↓-25.0%

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

The timing of crashes shifted year-over-year. The peak day for crashes moved from a tie between Thursday and Friday (37 crashes each) in the prior period to Saturday (35 crashes) in the current period. Similarly, the peak hour for collisions shifted from late morning at 11 a.m. (15 crashes) in 2023 to the evening at 7 p.m. (19 crashes) in 2024.

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 stable at one in both periods, the severity profile of non-fatal crashes changed significantly. The count of crashes resulting in serious injuries rose from 7 to 13, and their share of all crashes increased from 3.5% to 6.1%. Conversely, crashes involving minor or possible injuries decreased, with possible injury crashes falling from 37 incidents (18.3% share) to 27 incidents (12.7% share).

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
→0.0%prior 1
Serious Injury13serious injury crashes6.1%
↑85.7%prior 7
Minor Injury23minor injury crashes10.8%
↓-14.8%prior 27
Possible Injury27possible injury crashes12.7%
↓-27.0%prior 37
No Injury148no injury crashes69.8%
↑13.8%prior 130

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 with animals remained the top contributing factor in both years, though the count of these incidents decreased by 16% from 62 to 52. The ranking of other factors shifted, with crashes attributed to running a traffic signal more than doubling in count from 3 to 8. Crashes involving following too closely decreased from 12 to 8 incidents, while those involving a driver losing control remained stable, increasing by one from 15 to 16.

Officer-Reported Primary Contributing Cause

Animal52 (24.5%)↓-16.1%prior 62
Lost Control16 (7.5%)↑6.7%prior 15
Ran off road - straight12 (5.7%)↑20.0%prior 10
Ran Stop Sign11 (5.2%)↑22.2%prior 9
FTYROW: From stop sign10 (4.7%)→0.0%prior 10
Ran off road - left10 (4.7%)↑66.7%prior 6
Followed too close8 (3.8%)↓-33.3%prior 12
Ran Traffic Signal8 (3.8%)
Exceeded authorized speed7 (3.3%)
FTYROW: From yield sign7 (3.3%)

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 environmental conditions under which crashes occurred remained broadly similar year-over-year. The majority of incidents in both periods happened in clear weather and on dry road surfaces. Crashes in daylight increased from 101 to 112, while those in dark, unlit conditions were stable at 34 compared to 35 in the prior year. There were no significant proportional shifts in crashes occurring during adverse weather or poor lighting.

Weather

Clear117 (70.9%)
↑4.5%prior 112
Cloudy29 (17.6%)
↑38.1%prior 21
Rain8 (4.8%)
Snow5 (3.0%)
↓-16.7%prior 6
Other (explain in narrative)4 (2.4%)
Fog, smoke, smog2 (1.2%)

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

Lighting

Daylight112 (66.3%)
↑10.9%prior 101
Dark - roadway not lighted34 (20.1%)
↓-2.9%prior 35
Dark - roadway lighted12 (7.1%)
↑50.0%prior 8
Dark - unknown roadway lighting4 (2.4%)
Dusk4 (2.4%)
↓-20.0%prior 5
Dawn3 (1.8%)

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

Road Surface

Dry133 (79.6%)
↑11.8%prior 119
Wet15 (9.0%)
↑25.0%prior 12
Snow8 (4.8%)
Ice/frost5 (3.0%)
↓-37.5%prior 8
Gravel3 (1.8%)
↓-66.7%prior 9
Slush3 (1.8%)

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 saw a slight shift, as Ford's count increased from 40 to 52, making it the most common make, while Chevrolet's count decreased from 58 to 51. A more significant change occurred in the age demographics of persons involved in crashes. The number of individuals in the 26-34 age group dropped from 80 to 46, and those in the 65+ group fell from 74 to 48, indicating a shift in the age distribution of crash-involved persons.

Top Vehicle Makes (323 vehicles)

1
FORD52 (16.1%)
↑30.0%prior 40
2
CHEV39 (12.1%)
↓-18.8%prior 48
3
TOYO23 (7.1%)
↑76.9%prior 13
4
DODG19 (5.9%)
↑72.7%prior 11
5
TOYT18 (5.6%)
↑5.9%prior 17
6
JEEP15 (4.6%)
↑66.7%prior 9
7
GMC14 (4.3%)
↑27.3%prior 11
8
CHEVROLET12 (3.7%)
↑20.0%prior 10
9
HOND11 (3.4%)
→0.0%prior 11
10
HONDA9 (2.8%)

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

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

Sex Distribution (184 persons with recorded sex)

Male104 (56.5%)
↓-34.6%prior 159
Female80 (43.5%)
↓-30.4%prior 115

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: 212
  • Total persons involved: 332
  • Total vehicles involved: 323

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

ThatCarHitMe.com · An Injuria.ai Company