Yearly Traffic Safety Analysis

94 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Davis County, total traffic crashes decreased by 22.3%, from 121 incidents in 2023 to 94 in 2024. This downward trend was accompanied by a significant reduction in crash severity, with total fatalities falling from 4 to 1 and total injuries dropping from 36 to 21. The most substantial change was the 75% decrease in traffic-related deaths year-over-year.

94

▼ -22.3%was 121

Total Crash Events

1

▼ -75.0%was 4

Persons Killed

21

▼ -41.7%was 36

Persons Injured

1

▼ -75.0%was 4

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

Traffic safety trends in Davis County showed a notable improvement from 2023 to 2024. The total number of crashes fell from 121 to 94, a 22.3% decrease. This positive trend extended to crash outcomes, with fatalities dropping by 75% from 4 to 1, and injuries declining by 41.7% from 36 to 21.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 3↓-66.7%

21

Motorists Injured

Prior: 31↓-32.3%

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 temporal patterns of crashes shifted between the two periods. In 2024, the peak day for crashes was Monday with 19 incidents, a change from 2023 when Friday saw the most crashes at 24. The peak hour for collisions also shifted slightly earlier, moving from 7 p.m. in 2023 (12 crashes) to 6 p.m. in 2024 (11 crashes).

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

Crash severity decreased notably from 2023 to 2024. The number of fatal crashes dropped from 4 to 1, and their share of all crashes fell from 3.3% to 1.1%. While the number of serious injury crashes increased from 3 to 5, the count of minor injury crashes saw a sharp decline from 15 to 2. Consequently, the proportion of crashes resulting in no injuries rose from 72.7% in 2023 to 81.9% in 2024.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.1%
↓-75.0%prior 4
Serious Injury5serious injury crashes5.3%
↑66.7%prior 3
Minor Injury2minor injury crashes2.1%
↓-86.7%prior 15
Possible Injury9possible injury crashes9.6%
↓-18.2%prior 11
No Injury77no injury crashes81.9%
↓-12.5%prior 88

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 an animal remained the leading contributing factor in both periods, though the count decreased from 38 crashes in 2023 to 30 in 2024. 'Lost Control' was a consistent factor, accounting for 10 crashes in both years, which increased its share of total factors from 8.3% to 10.6%. Incidents related to 'Failure to Yield Right of Way: From stop sign' saw a reduction in count, from 10 in 2023 to 8 in 2024.

Officer-Reported Primary Contributing Cause

Animal30 (31.9%)↓-21.1%prior 38
Lost Control10 (10.6%)→0.0%prior 10
FTYROW: From stop sign8 (8.5%)↓-20.0%prior 10
Followed too close4 (4.3%)↓-33.3%prior 6
Operating vehicle in an reckless, erratic, careless, negligent manner4 (4.3%)
FTYROW: From driveway4 (4.3%)
FTYROW: Making left turn3 (3.2%)
Ran off road - straight3 (3.2%)↓-40.0%prior 5
FTYROW: Other (explain in narrative)2 (2.1%)
Other (explain in narrative): Other2 (2.1%)↓-80.0%prior 10

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

Road & Environmental Conditions

Crashes in both years occurred most frequently during daylight hours (52 in 2024 vs. 63 in 2023) and in clear weather conditions (52 vs. 70). The number of incidents on dry road surfaces decreased from 69 in 2023 to 50 in 2024, mirroring the overall decline in crashes. Crashes occurring in dark conditions also saw a reduction, falling from 27 incidents in 2023 to 17 in 2024.

Weather

Clear52 (76.5%)
↓-25.7%prior 70
Cloudy10 (14.7%)
↑11.1%prior 9
Rain3 (4.4%)
Snow3 (4.4%)
↓-40.0%prior 5

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

Lighting

Daylight52 (72.2%)
↓-17.5%prior 63
Dark - roadway not lighted9 (12.5%)
↓-50.0%prior 18
Dark - roadway lighted5 (6.9%)
↓-37.5%prior 8
Dark - unknown roadway lighting3 (4.2%)
Dusk3 (4.2%)

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

Road Surface

Dry50 (73.5%)
↓-27.5%prior 69
Wet6 (8.8%)
→0.0%prior 6
Gravel5 (7.4%)
↓-44.4%prior 9
Ice/frost3 (4.4%)
Snow3 (4.4%)
Mud, dirt1 (1.5%)

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

Vehicles & Demographics

Ford and Chevrolet remained the two most common vehicle makes involved in crashes in both 2023 and 2024, with Ford's count increasing from 38 to 40 and Chevrolet's decreasing from 38 to 29. The demographic profile of persons involved in crashes showed significant changes, particularly a large decrease in the 65+ age group, from 54 individuals in 2023 to 20 in 2024. The 16-20 age group also saw a substantial reduction in involvement, from 47 persons to 26.

Top Vehicle Makes (141 vehicles)

1
FORD40 (28.4%)
↑5.3%prior 38
2
CHEV24 (17%)
↓-20.0%prior 30
3
JEEP8 (5.7%)
↓-20.0%prior 10
4
DODG6 (4.3%)
↓-53.8%prior 13
5
TOYOTA5 (3.5%)
6
CHEVROLET5 (3.5%)
↓-37.5%prior 8
7
TOYT5 (3.5%)
↓-44.4%prior 9
8
BUIC5 (3.5%)
→0.0%prior 5
9
GMC3 (2.1%)
↓-66.7%prior 9
10
KIA3 (2.1%)

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

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

Sex Distribution (82 persons with recorded sex)

Male55 (67.1%)
↓-47.1%prior 104
Female27 (32.9%)
↓-57.1%prior 63

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: 94
  • Total persons involved: 146
  • Total vehicles involved: 141

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