Yearly Traffic Safety Analysis

98 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Shelby County, the total number of crashes was unchanged year-over-year, with 98 incidents recorded in both 2024 and 2023. While the overall crash volume remained stable, the number of fatalities doubled from one in 2023 to two in 2024. The most significant shift in contributing factors was a sharp decrease in crashes involving animals.

98

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

31

▼ -6.1%was 33

Persons Injured

2

▲ 100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 crash trends in Shelby County show stability in volume, with 98 total crashes in both 2024 and 2023. However, the severity of outcomes shifted, as total fatalities increased from one to two, while total injuries saw a slight decrease from 33 to 31. This indicates a stable number of incidents but a year-over-year increase in fatal outcomes.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

2

Cyclists Injured

Prior: 0→%

29

Motorists Injured

Prior: 31↓-6.5%

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

The temporal patterns of crashes remained largely consistent year-over-year. Friday was the peak day for crashes in both 2024 (20 crashes) and 2023 (22 crashes). The peak hour shifted slightly earlier, from 3 p.m. in 2023 (12 crashes) to 2 p.m. in 2024 (9 crashes). A notable daily change was an increase in Sunday crashes from 6 to 12 and a decrease in Wednesday crashes from 20 to 14.

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 severity of crashes increased in 2024 compared to the prior year. The number of fatal crashes doubled from one to two, and serious injury crashes also doubled from three to six. This increase in severe outcomes was offset by a decrease in less severe incidents, with minor injury crashes falling from 14 to 11 and possible injury crashes dropping from 12 to 6. Consequently, the share of crashes resulting in no injuries rose from 69.4% to 74.5%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2%
↑100.0%prior 1
Serious Injury6serious injury crashes6.1%
↑100.0%prior 3
Minor Injury11minor injury crashes11.2%
↓-21.4%prior 14
Possible Injury6possible injury crashes6.1%
↓-50.0%prior 12
No Injury73no injury crashes74.5%
↑7.4%prior 68

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

The leading contributing factors showed some shifts between periods. Crashes involving an animal remained the top factor but saw a significant count-based decrease from 19 incidents in 2023 to 10 in 2024. The count for 'Lost Control' was unchanged at 8 crashes, while 'Followed too close' decreased slightly from 8 to 7 crashes. 'Driver Distraction: Other interior distraction' was a growing factor, with its crash count increasing from 4 to 6.

Officer-Reported Primary Contributing Cause

Animal10 (10.2%)↓-47.4%prior 19
Lost Control8 (8.2%)→0.0%prior 8
Followed too close7 (7.1%)↓-12.5%prior 8
FTYROW: From stop sign6 (6.1%)↑20.0%prior 5
Driver Distraction: Other interior distraction6 (6.1%)
Driving too fast for conditions6 (6.1%)↓-14.3%prior 7
Other (explain in narrative): Other6 (6.1%)
Ran off road - straight5 (5.1%)
FTYROW: Making left turn5 (5.1%)
Other (explain in narrative): No improper action5 (5.1%)

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

Road & Environmental Conditions

Crash conditions were broadly similar year-over-year, with the majority of incidents in both periods occurring in clear weather on dry roads during daylight hours. In 2024, there was a slight increase in crashes under adverse conditions, with incidents in 'Freezing rain/drizzle' increasing from one to four. Crashes on dark, unlighted roadways also rose from 12 to 16, while crashes on dry roads remained the dominant scenario, increasing from 63 to 65.

Weather

Clear67 (77.0%)
↓-1.5%prior 68
Cloudy8 (9.2%)
↓-20.0%prior 10
Freezing rain/drizzle4 (4.6%)
Rain3 (3.4%)
Fog, smoke, smog3 (3.4%)
Other (explain in narrative)1 (1.1%)
Blowing Snow1 (1.1%)

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

Lighting

Daylight60 (68.2%)
↑1.7%prior 59
Dark - roadway not lighted16 (18.2%)
↑33.3%prior 12
Dark - roadway lighted6 (6.8%)
→0.0%prior 6
Dawn2 (2.3%)
Dusk2 (2.3%)
Dark - unknown roadway lighting2 (2.3%)

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

Road Surface

Dry65 (73.9%)
↑3.2%prior 63
Wet8 (9.1%)
→0.0%prior 8
Ice/frost4 (4.5%)
↓-20.0%prior 5
Snow4 (4.5%)
Slush3 (3.4%)
Gravel2 (2.3%)
Sand1 (1.1%)
Other (explain in narrative)1 (1.1%)

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

Vehicles & Demographics

Comparing vehicles involved, Chevrolet (including CHEV and CHEVROLET designations) saw an increase in crash involvement from 32 vehicles in 2023 to 49 in 2024, becoming the most common make. Ford-made vehicles saw a decrease from 33 to 28. The age distribution of persons involved showed a significant drop in the 55-64 age group, from 35 individuals to 12, while the 45-54 age group increased from 25 to 29.

Top Vehicle Makes (154 vehicles)

1
CHEV33 (21.4%)
↑32.0%prior 25
2
FORD28 (18.2%)
↓-15.2%prior 33
3
CHEVROLET16 (10.4%)
↑128.6%prior 7
4
GMC9 (5.8%)
↑80.0%prior 5
5
KIA6 (3.9%)
↑20.0%prior 5
6
JEEP5 (3.2%)
→0.0%prior 5
7
DODG5 (3.2%)
8
DODGE5 (3.2%)
9
HOND4 (2.6%)
10
BUIC3 (1.9%)
↓-40.0%prior 5

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

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

Sex Distribution (101 persons with recorded sex)

Male62 (61.4%)
↓-20.5%prior 78
Female39 (38.6%)
↓-35.0%prior 60

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: 98
  • Total persons involved: 162
  • Total vehicles involved: 154

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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