Yearly Traffic Safety Analysis

193 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Grundy County, total crashes decreased from 213 in the prior period to 193 in the current period, a 9.4% reduction. The most significant year-over-year change was the reduction in traffic fatalities, which fell from 4 to 0. Total injuries also saw a substantial decrease of 50%, from 90 to 45.

193

▼ -9.4%was 213

Total Crash Events

0

▼ -100.0%was 4

Persons Killed

45

▼ -50.0%was 90

Persons Injured

0

▼ -100.0%was 1

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 traffic safety trends in Grundy County improved year-over-year. Total crashes fell by 9.4%, from 213 to 193. The number of people injured in these incidents was halved, dropping from 90 to 45, while fatalities were eliminated, decreasing from 4 to 0.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 4↓-100.0%

1

Pedestrians Injured

Prior: 1→0.0%

44

Motorists Injured

Prior: 88↓-50.0%

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 showed notable shifts between the two periods. The peak day for crashes moved from Thursday (39 incidents) in the prior year to Friday (31 incidents) in the current year. The peak hour for collisions also changed, shifting from the 5 p.m. hour (19 crashes) in the prior period to the 9 p.m. hour (14 crashes) in the current period.

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 improved significantly year-over-year. The county recorded zero fatal crashes in the current period, down from one fatal crash that resulted in four deaths in the prior year. The proportion of crashes involving any level of injury also decreased, from 31.5% of all incidents (67 crashes) to 21.8% (42 crashes). While the number of serious injury crashes remained stable at 6, minor and possible injury crashes saw a combined reduction from 61 to 36.

Outcome by Severity (Crash Events)

Serious Injury6serious injury crashes3.1%
→0.0%prior 6
Minor Injury20minor injury crashes10.4%
↓-35.5%prior 31
Possible Injury16possible injury crashes8.3%
↓-46.7%prior 30
No Injury151no injury crashes78.2%
↑4.1%prior 145

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 animals remained the leading contributing factor in both periods, with an identical count of 86 incidents, though their share of total crashes rose from 40.4% to 44.6%. The number of crashes attributed to 'Lost Control' decreased from 19 to 12, while incidents of 'Ran off road - left' also saw a slight decline from 17 to 15. Conversely, crashes where a vehicle 'Ran off road - straight' more than tripled, increasing from 4 in the prior period to 13 in the current period.

Officer-Reported Primary Contributing Cause

Animal86 (44.6%)→0.0%prior 86
Ran off road - left15 (7.8%)↓-11.8%prior 17
Ran off road - straight13 (6.7%)
Lost Control12 (6.2%)↓-36.8%prior 19
FTYROW: From stop sign8 (4.1%)
Driver Distraction: Other interior distraction6 (3.1%)→0.0%prior 6
Operating vehicle in an reckless, erratic, careless, negligent manner6 (3.1%)→0.0%prior 6
Followed too close6 (3.1%)
Driving too fast for conditions5 (2.6%)↓-44.4%prior 9
Other (explain in narrative): Other3 (1.6%)↓-57.1%prior 7

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 remained broadly similar year-over-year, with most incidents in both periods occurring on dry roads under clear skies. However, there was a notable increase in crashes occurring in 'Dark - roadway not lighted' conditions, which rose from 32 to 41 incidents. Crashes on snowy road surfaces increased from 6 to 9, while those on icy or frosty surfaces decreased slightly from 15 to 14.

Weather

Clear73 (60.3%)
↓-13.1%prior 84
Cloudy23 (19.0%)
↑9.5%prior 21
Snow10 (8.3%)
↓-16.7%prior 12
Rain4 (3.3%)
↓-42.9%prior 7
Fog, smoke, smog4 (3.3%)
Blowing Snow3 (2.5%)
Freezing rain/drizzle3 (2.5%)
Severe Winds1 (0.8%)

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

Lighting

Daylight65 (53.3%)
↓-22.6%prior 84
Dark - roadway not lighted41 (33.6%)
↑28.1%prior 32
Dark - roadway lighted9 (7.4%)
↓-35.7%prior 14
Dusk4 (3.3%)
Dawn2 (1.6%)
Dark - unknown roadway lighting1 (0.8%)

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

Road Surface

Dry78 (64.5%)
↓-11.4%prior 88
Ice/frost14 (11.6%)
↓-6.7%prior 15
Wet9 (7.4%)
↓-30.8%prior 13
Snow9 (7.4%)
↑50.0%prior 6
Gravel7 (5.8%)
↓-12.5%prior 8
Slush4 (3.3%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes were consistent, with Ford (56 current vs. 53 prior) and Chevrolet (45 current vs. 49 prior) remaining the top two in both years. However, the age demographics of persons involved in crashes shifted significantly. In the prior period, the 65+ (71 people) and 55-64 (66 people) age groups were most frequently involved. In the current period, involvement for these groups dropped to 26 and 28 people, respectively, while the 26-34 age group became the most represented with 44 individuals involved.

Top Vehicle Makes (258 vehicles)

1
FORD56 (21.7%)
↑5.7%prior 53
2
CHEV45 (17.4%)
↓-8.2%prior 49
3
JEEP14 (5.4%)
↓-6.7%prior 15
4
CHEVROLET13 (5%)
↑44.4%prior 9
5
TOYO13 (5%)
→0.0%prior 13
6
GMC12 (4.7%)
↑140.0%prior 5
7
DODG8 (3.1%)
→0.0%prior 8
8
BUIC8 (3.1%)
↓-11.1%prior 9
9
CHRY7 (2.7%)
↓-12.5%prior 8
10
HOND7 (2.7%)
↑40.0%prior 5

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

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

Sex Distribution (124 persons with recorded sex)

Male85 (68.5%)
↓-44.8%prior 154
Female39 (31.5%)
↓-63.2%prior 106

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 10, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 193
  • Total persons involved: 264
  • Total vehicles involved: 258

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 10, 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