Yearly Traffic Safety Analysis

2,513 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Johnson County, total traffic crashes increased by 7.1% from 2,346 in 2023 to 2,513 in 2024. While the number of fatalities decreased from 10 to 8, the most notable year-over-year shift was an 87% increase in the count of crashes attributed to "Driving too fast for conditions," which rose from 107 to 200 incidents.

2,513

▲ 7.1%was 2,346

Total Crash Events

8

▼ -20.0%was 10

Persons Killed

709

▲ 10.6%was 641

Persons Injured

8

▼ -11.1%was 9

Fatal Crash Events

Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (8) 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

Crash data for Johnson County indicates an upward trend in overall crash and injury volume from 2023 to 2024. Total crashes rose by 7.1%, from 2,346 to 2,513 incidents. Correspondingly, the number of people injured in these crashes increased by 10.6%, from 641 to 709, even as total fatalities declined from 10 to 8.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2↓-50.0%

1

Cyclists Killed

Prior: 0→%

6

Motorists Killed

Prior: 8↓-25.0%

0

Other Killed

Prior: 0→0.0%

31

Pedestrians Injured

Prior: 19↑63.2%

38

Cyclists Injured

Prior: 25↑52.0%

628

Motorists Injured

Prior: 594↑5.7%

12

Other Injured

Prior: 3↑300.0%

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 slightly between the two periods. In 2024, Monday became the peak day for crashes with 412 incidents, changing from Friday in 2023 which saw 410 crashes. The daily peak for collisions also moved an hour later, from the 4 p.m. hour in 2023 (238 crashes) to the 5 p.m. hour in 2024 (240 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

While the total number of fatal crashes decreased from 9 in 2023 to 8 in 2024, the severity of injury-related incidents increased. The proportion of crashes resulting in a serious injury grew from 1.7% of all crashes (39 incidents) in the prior year to 2.3% (59 incidents) in the current year. Consequently, the share of crashes with no reported injuries fell from 76.6% to 75.1%.

Outcome by Severity (Crash Events)

Fatal8fatal crashes0.3%
↓-11.1%prior 9
Serious Injury59serious injury crashes2.3%
↑51.3%prior 39
Minor Injury242minor injury crashes9.6%
↑14.2%prior 212
Possible Injury316possible injury crashes12.6%
↑9.0%prior 290
No Injury1,888no injury crashes75.1%
↑5.1%prior 1,796

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

Although "Followed too close" remained the top contributing factor in both years, its count decreased from 441 in 2023 to 386 in 2024. The most significant change was an 87% increase in the count of crashes attributed to "Driving too fast for conditions," which jumped from 107 to 200 incidents, making it the second-most cited factor in 2024. Incidents involving a driver running a stop sign also rose substantially, from 47 to 75, a 60% increase in count.

Officer-Reported Primary Contributing Cause

Followed too close386 (15.4%)↓-12.5%prior 441
Driving too fast for conditions200 (8%)↑86.9%prior 107
Ran off road - left156 (6.2%)↑15.6%prior 135
Other (explain in narrative): Other155 (6.2%)→0.0%prior 155
Animal112 (4.5%)↓-13.2%prior 129
Improper or erratic lane changing110 (4.4%)↑13.4%prior 97
FTYROW: Making left turn103 (4.1%)↑6.2%prior 97
FTYROW: From stop sign95 (3.8%)↑13.1%prior 84
Ran Traffic Signal94 (3.7%)↑3.3%prior 91
Made improper turn77 (3.1%)↓-12.5%prior 88

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 under adverse conditions increased from 2023 to 2024. Crashes on non-dry road surfaces like wet, snow, or ice accounted for 24.2% of all incidents in 2024, a notable increase from 16.5% in the prior year. Similarly, the share of crashes in non-clear weather conditions grew from 29.6% to 32.5%, while the distribution of crashes by lighting conditions remained stable.

Weather

Clear1,695 (69.8%)
↑2.7%prior 1,651
Cloudy402 (16.6%)
↑5.5%prior 381
Rain129 (5.3%)
↑26.5%prior 102
Snow119 (4.9%)
↑21.4%prior 98
Blowing Snow30 (1.2%)
Freezing rain/drizzle24 (1.0%)
↑33.3%prior 18
Fog, smoke, smog18 (0.7%)
↑200.0%prior 6
Other (explain in narrative)7 (0.3%)
Sleet, hail3 (0.1%)
Severe Winds2 (0.1%)

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

Lighting

Daylight1,823 (74.5%)
↑7.8%prior 1,691
Dark - roadway lighted314 (12.8%)
↓-2.8%prior 323
Dark - roadway not lighted201 (8.2%)
↑33.1%prior 151
Dusk59 (2.4%)
↓-10.6%prior 66
Dawn33 (1.3%)
↓-5.7%prior 35
Dark - unknown roadway lighting18 (0.7%)
↑20.0%prior 15

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

Road Surface

Dry1,817 (74.6%)
↓-2.9%prior 1,872
Wet282 (11.6%)
↑22.6%prior 230
Snow180 (7.4%)
↑114.3%prior 84
Ice/frost131 (5.4%)
↑172.9%prior 48
Slush15 (0.6%)
↓-40.0%prior 25
Gravel9 (0.4%)
→0.0%prior 9
Other (explain in narrative)1 (0.0%)

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

Vehicles & Demographics

The ranking of the most common vehicle makes involved in crashes was stable, with Ford, Chevrolet, Toyota, and Honda leading in both years, and counts for each increasing in 2024. An analysis of the age of persons involved in crashes shows a decrease in counts for several adult age groups compared to the prior year. Notably, the number of individuals in the 21-25 age group involved in crashes fell from 804 in 2023 to 645 in 2024.

Top Vehicle Makes (4,671 vehicles)

1
FORD648 (13.9%)
↑7.8%prior 601
2
CHEV455 (9.7%)
↑7.3%prior 424
3
TOYT410 (8.8%)
↑9.6%prior 374
4
HOND294 (6.3%)
↑3.5%prior 284
5
JEEP205 (4.4%)
↑15.2%prior 178
6
NISS186 (4%)
↑1.6%prior 183
7
KIA146 (3.1%)
↑23.7%prior 118
8
CHEVROLET145 (3.1%)
↑2.1%prior 142
9
TOYOTA144 (3.1%)
↓-7.1%prior 155
10
HYUN140 (3%)
↑27.3%prior 110

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

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

Sex Distribution (3,515 persons with recorded sex)

Male1,921 (54.7%)
↓-13.5%prior 2,220
Female1,594 (45.3%)
↓-12.4%prior 1,819

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: 2,513
  • Total persons involved: 4,837
  • Total vehicles involved: 4,671

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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