Yearly Traffic Safety Analysis

699 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In 2024, Lee County recorded 699 total crashes, a 1.8% decrease from the 712 crashes reported in 2023. Despite this slight overall decline, the number of injuries increased by 16% from 144 to 167. The most notable year-over-year shift was a 52.2% reduction in crashes attributed to driving under the influence, which fell from 23 incidents in 2023 to 11 in 2024.

699

▼ -1.8%was 712

Total Crash Events

4

▼ -20.0%was 5

Persons Killed

167

▲ 16.0%was 144

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 Lee County was slightly downward in 2024, with total incidents falling by 1.8% from 712 to 699. However, this decrease in crash volume was accompanied by a 16.0% increase in the number of people injured, which rose from 144 to 167. The number of fatalities saw a small decline from 5 in 2023 to 4 in 2024.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2↓-50.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 3→0.0%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 4↓-50.0%

3

Cyclists Injured

Prior: 6↓-50.0%

161

Motorists Injured

Prior: 134↑20.1%

1

Other Injured

Prior: 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 temporal patterns of crashes shifted year-over-year. The peak day for crashes moved from Thursday (132 crashes) in 2023 to Friday (120 crashes) in 2024. The single busiest hour also shifted slightly earlier, from the 6 p.m. hour in 2023, which saw 61 crashes, to the 5 p.m. hour in 2024 with 52 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

The number of fatal crashes remained stable at 4 for both 2024 and 2023, with the fatal crash rate holding steady at approximately 0.6% of all incidents. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) increased slightly from 20.8% in 2023 to 21.3% in 2024. This was driven by an increase in crashes with possible injuries, which rose from 69 to 80, and serious injuries, which increased from 17 to 18.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.6%
→0.0%prior 4
Serious Injury18serious injury crashes2.6%
↑5.9%prior 17
Minor Injury51minor injury crashes7.3%
↓-17.7%prior 62
Possible Injury80possible injury crashes11.4%
↑15.9%prior 69
No Injury546no injury crashes78.1%
↓-2.5%prior 560

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 were the leading contributing factor in both periods, with the count increasing by 5.4% from 261 incidents in 2023 to 275 in 2024. The top five factors were consistent across both years, though their counts shifted. Crashes attributed to 'Followed too close' decreased from 39 to 33, and those involving 'FTYROW: From stop sign' fell from 36 to 31.

Officer-Reported Primary Contributing Cause

Animal275 (39.3%)↑5.4%prior 261
Lost Control40 (5.7%)↓-2.4%prior 41
Other (explain in narrative): Other33 (4.7%)↓-29.8%prior 47
Followed too close33 (4.7%)↓-15.4%prior 39
FTYROW: From stop sign31 (4.4%)↓-13.9%prior 36
Ran off road - left30 (4.3%)↑50.0%prior 20
Ran Stop Sign26 (3.7%)↓-10.3%prior 29
Driver Distraction: Other interior distraction25 (3.6%)↓-7.4%prior 27
Ran off road - straight19 (2.7%)↓-24.0%prior 25
FTYROW: Making left turn16 (2.3%)↑6.7%prior 15

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 dry road surfaces decreased from 86.0% in 2023 to 77.2% in 2024. Concurrently, there was a significant increase in the number of crashes on adverse surfaces, with incidents on icy or frosty roads rising from 8 to 28 and crashes on snowy roads increasing from 7 to 23. While daylight and clear weather still accounted for the majority of crashes, their overall share of incidents declined in 2024.

Weather

Clear314 (72.2%)
↓-15.6%prior 372
Cloudy61 (14.0%)
↑3.4%prior 59
Rain16 (3.7%)
↑6.7%prior 15
Snow15 (3.4%)
↑66.7%prior 9
Freezing rain/drizzle11 (2.5%)
↑83.3%prior 6
Fog, smoke, smog9 (2.1%)
Blowing Snow4 (0.9%)
Other (explain in narrative)3 (0.7%)
Severe Winds1 (0.2%)
Blowing sand, soil, dirt1 (0.2%)

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

Lighting

Daylight285 (63.1%)
↓-5.9%prior 303
Dark - roadway not lighted67 (14.8%)
↓-21.2%prior 85
Dark - roadway lighted54 (11.9%)
↑10.2%prior 49
Dark - unknown roadway lighting21 (4.6%)
↑162.5%prior 8
Dusk13 (2.9%)
↓-38.1%prior 21
Dawn12 (2.7%)
↑71.4%prior 7

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

Road Surface

Dry336 (77.2%)
↓-15.6%prior 398
Wet32 (7.4%)
↓-8.6%prior 35
Ice/frost28 (6.4%)
↑250.0%prior 8
Snow23 (5.3%)
↑228.6%prior 7
Gravel10 (2.3%)
↓-9.1%prior 11
Slush5 (1.1%)
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes were consistent year-over-year, with Ford and Chevrolet models leading in both periods. The number of Fords involved increased from 169 to 189, while combined Chevrolet models rose from 195 to 200. Analysis of the age of persons involved shows a proportional increase in the 16-20 age group, which grew from 10.3% of individuals in 2023 to 13.0% in 2024. Conversely, the 26-34 age group's share fell from 18.1% to 16.5%.

Top Vehicle Makes (1,015 vehicles)

1
FORD189 (18.6%)
↑11.8%prior 169
2
CHEV126 (12.4%)
→0.0%prior 126
3
CHEVROLET74 (7.3%)
↑7.2%prior 69
4
GMC51 (5%)
↓-19.0%prior 63
5
JEEP48 (4.7%)
↓-9.4%prior 53
6
DODG47 (4.6%)
↑9.3%prior 43
7
KIA43 (4.2%)
→0.0%prior 43
8
TOYT33 (3.3%)
↓-5.7%prior 35
9
HOND28 (2.8%)
↑16.7%prior 24
10
CHRY25 (2.5%)
↓-13.8%prior 29

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

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

Sex Distribution (481 persons with recorded sex)

Male265 (55.1%)
↓-47.8%prior 508
Female216 (44.9%)
↓-47.7%prior 413

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: 699
  • Total persons involved: 1,040
  • Total vehicles involved: 1,015

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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