Yearly Traffic Safety Analysis

222 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In 2024, Louisa County recorded 222 total crashes, a 5.7% increase from the 210 crashes reported in 2023. Despite the rise in total incidents, the most significant year-over-year change was a complete elimination of traffic fatalities, which fell from 7 in the prior period to 0 in the current period. The total number of injuries saw a slight increase from 48 to 53.

222

▲ 5.7%was 210

Total Crash Events

0

▼ -100.0%was 7

Persons Killed

53

▲ 10.4%was 48

Persons Injured

0

▼ -100.0%was 5

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 · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, total crashes in Louisa County saw a modest increase of 5.7%, rising from 210 in 2023 to 222 in 2024. The number of injuries also rose by 10.4%, from 48 to 53. However, there was a significant positive trend in crash severity, as fatalities dropped from 7 to 0 year-over-year, and fatal crashes fell from 5 to 0.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 7↓-100.0%

1

Cyclists Injured

Prior: 1→0.0%

52

Motorists Injured

Prior: 47↑10.6%

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 between the two periods. The peak day for crashes moved from Wednesday (36 crashes) in 2023 to Thursday (44 crashes) in 2024. More dramatically, the peak hour for collisions shifted from the evening commute at 5 p.m. in the prior year (19 crashes) to the early morning at 5 a.m. in the current year (28 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 saw a significant improvement in 2024, with fatal crashes dropping to zero from 5 incidents (2.4% of all crashes) in 2023. The proportion of crashes resulting in serious injuries remained stable at 1.4% in both periods. Crashes categorized with possible injuries increased their share from 5.7% in 2023 to 9.5% in 2024, while minor injury crashes saw their share decrease from 9.5% to 6.8%.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes1.4%
→0.0%prior 3
Minor Injury15minor injury crashes6.8%
↓-25.0%prior 20
Possible Injury21possible injury crashes9.5%
↑75.0%prior 12
No Injury183no injury crashes82.4%
↑7.6%prior 170

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, with the count increasing by 10.4% from 115 crashes in 2023 to 127 in 2024. 'Lost Control' incidents also rose, increasing by 66.7% from 9 to 15 crashes, making it the second-leading factor in the current period. Notably, crashes attributed to 'Ran Stop Sign' increased from 1 to 5, and 'Ran off road - left' incidents grew from 8 to 14.

Officer-Reported Primary Contributing Cause

Animal127 (57.2%)↑10.4%prior 115
Lost Control15 (6.8%)↑66.7%prior 9
Ran off road - left14 (6.3%)↑75.0%prior 8
Other (explain in narrative): Other8 (3.6%)↑14.3%prior 7
Ran off road - straight6 (2.7%)↓-33.3%prior 9
FTYROW: From stop sign5 (2.3%)↓-28.6%prior 7
Ran Stop Sign5 (2.3%)
Operating vehicle in an reckless, erratic, careless, negligent manner4 (1.8%)
Followed too close4 (1.8%)
Driving too fast for conditions4 (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

Driving conditions for crashes remained broadly consistent year-over-year, with the majority of incidents in both periods occurring in clear weather on dry roads. In 2024, crashes in daylight accounted for 48.6% of incidents with known lighting conditions, a slight decrease from 53% in 2023. Crashes on dry road surfaces represented 76.1% of the total in the current period, compared to 78.1% in the prior period.

Weather

Clear76 (71.7%)
↓-12.6%prior 87
Cloudy14 (13.2%)
↑7.7%prior 13
Rain6 (5.7%)
↓-14.3%prior 7
Snow5 (4.7%)
→0.0%prior 5
Fog, smoke, smog2 (1.9%)
Freezing rain/drizzle1 (0.9%)
Severe Winds1 (0.9%)
Sleet, hail1 (0.9%)

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

Lighting

Daylight54 (48.6%)
↓-11.5%prior 61
Dark - roadway not lighted39 (35.1%)
→0.0%prior 39
Dark - roadway lighted6 (5.4%)
↓-33.3%prior 9
Dusk5 (4.5%)
Dawn4 (3.6%)
Dark - unknown roadway lighting3 (2.7%)

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

Road Surface

Dry83 (76.1%)
↓-10.8%prior 93
Wet12 (11.0%)
↑9.1%prior 11
Snow7 (6.4%)
Ice/frost5 (4.6%)
Gravel2 (1.8%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes saw a minor shift, with Chevrolet and Ford tying for the top spot in 2024 with 37 vehicles each; in 2023, Ford led with 40 vehicles to Chevrolet's 34. The 35-44 age group represented the largest share of persons involved in crashes in both years, accounting for 20.8% in 2024 and 20.2% in 2023. The total number of people involved in crashes decreased from 410 to 279 year-over-year.

Top Vehicle Makes (260 vehicles)

1
CHEV37 (14.2%)
↑8.8%prior 34
2
FORD37 (14.2%)
↓-7.5%prior 40
3
NISS16 (6.2%)
↑60.0%prior 10
4
HOND14 (5.4%)
↑55.6%prior 9
5
DODG14 (5.4%)
→0.0%prior 14
6
JEEP11 (4.2%)
→0.0%prior 11
7
TOYT10 (3.8%)
→0.0%prior 10
8
KIA10 (3.8%)
↑11.1%prior 9
9
GMC10 (3.8%)
↓-16.7%prior 12
10
CHEVROLET9 (3.5%)
↑12.5%prior 8

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

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

Sex Distribution (99 persons with recorded sex)

Male68 (68.7%)
↓-56.4%prior 156
Female31 (31.3%)
↓-66.7%prior 93

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: 222
  • Total persons involved: 279
  • Total vehicles involved: 260

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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