Monthly Traffic Safety Analysis

5,119 CRASHES IN
IOWA, IA
OCTOBER 2024

All metrics benchmarked againstOctober 2023

In October 2024, Iowa recorded 5,119 traffic crashes, a 2.5% increase from the 4,992 crashes in October 2023. While the overall crash volume saw a modest rise, the number of fatalities increased significantly. There were 40 fatalities in the current period compared to 28 in the prior year, marking a 42.9% year-over-year increase.

5,119

▲ 2.5%was 4,992

Total Crash Events

40

▲ 42.9%was 28

Persons Killed

1,549

▲ 5.7%was 1,466

Persons Injured

37

▲ 42.3%was 26

Fatal Crash Events

Note: "Persons Killed" (40) counts individual fatalities across all crash events. "Fatal" in the severity table below (37) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends worsened year-over-year. Total crashes rose by 2.5% from 4,992 to 5,119. More significantly, total injuries increased by 5.7% to 1,549, and total fatalities saw a sharp 42.9% rise from 28 to 40.

Vulnerable Road User Casualties

6

Pedestrians Killed

Prior: 0→%

1

Cyclists Killed

Prior: 1→0.0%

33

Motorists Killed

Prior: 27↑22.2%

0

Other Killed

Prior: 0→0.0%

40

Pedestrians Injured

Prior: 35↑14.3%

53

Cyclists Injured

Prior: 31↑71.0%

1,447

Motorists Injured

Prior: 1,396↑3.7%

9

Other Injured

Prior: 4↑125.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed a slight shift between the two periods. The peak day for crashes moved from Tuesday (842 crashes) in the prior year to Wednesday (919 crashes) in the current period. However, the peak hour for collisions remained consistent at 3 p.m. in both October 2023 (447 crashes) and October 2024 (445 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased year-over-year. The proportion of fatal crashes rose from 0.5% to 0.7% of all incidents. Similarly, the share of crashes involving serious injuries grew from 1.6% to 2.1%. While crashes resulting in possible injury saw a slight proportional decrease, the overall share of crashes involving any level of injury (fatal, serious, minor, or possible) increased from 26.0% to 27.4%.

Severity is per crash event (most severe injury). 37 fatal crash events resulted in 40 persons killed.

Outcome by Severity (Crash Events)

Fatal37fatal crashes0.7%
↑42.3%prior 26
Serious Injury107serious injury crashes2.1%
↑30.5%prior 82
Minor Injury507minor injury crashes9.9%
↑12.9%prior 449
Possible Injury754possible injury crashes14.7%
↑1.2%prior 745
No Injury3,714no injury crashes72.6%
↑0.7%prior 3,690

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the top contributing factor in both periods, though the count decreased slightly from 964 to 943. 'Followed too close' remained the second-leading factor and increased in count from 535 to 587. 'Failure to yield right of way from a stop sign' moved into the top three factors in the current period, with its count increasing from 270 to 295. Conversely, crashes attributed to 'Lost Control' decreased from 207 to 181.

Officer-Reported Primary Contributing Cause

Animal943 (18.4%)↓-2.2%prior 964
Followed too close587 (11.5%)↑9.7%prior 535
FTYROW: From stop sign295 (5.8%)↑9.3%prior 270
Ran off road - left260 (5.1%)↑7.0%prior 243
Other (explain in narrative): Other237 (4.6%)↓-19.9%prior 296
FTYROW: Making left turn221 (4.3%)↑0.9%prior 219
Lost Control181 (3.5%)↓-12.6%prior 207
Driver Distraction: Other interior distraction176 (3.4%)↑2.3%prior 172
Ran Traffic Signal159 (3.1%)↓-5.9%prior 169
Ran Stop Sign142 (2.8%)↓-3.4%prior 147

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in October 2024 occurred under generally better conditions than in the previous year, despite the overall increase in collisions. The proportion of crashes on dry roads rose from 68.9% to 77.9%, while crashes in clear weather increased from 58.3% to 74.8% of the total. Consequently, the share of crashes happening in adverse conditions like rain (2.2% vs. 6.4%) and on wet roads (3.8% vs. 12.5%) was substantially lower in the current period.

Weather

Clear3,829 (89.5%)
↑31.6%prior 2,910
Cloudy307 (7.2%)
↓-65.9%prior 899
Rain115 (2.7%)
↓-63.8%prior 318
Severe Winds10 (0.2%)
↑42.9%prior 7
Blowing sand, soil, dirt7 (0.2%)
Fog, smoke, smog6 (0.1%)
↓-33.3%prior 9
Other (explain in narrative)3 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · Weather condition at time of crash

Lighting

Daylight3,056 (70.3%)
↑7.8%prior 2,836
Dark - roadway lighted593 (13.6%)
↓-10.8%prior 665
Dark - roadway not lighted384 (8.8%)
↓-25.6%prior 516
Dusk141 (3.2%)
↑46.9%prior 96
Dawn101 (2.3%)
↑2.0%prior 99
Dark - unknown roadway lighting70 (1.6%)
↑311.8%prior 17

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · Lighting condition field

Road Surface

Dry3,988 (93.1%)
↑16.0%prior 3,439
Wet194 (4.5%)
↓-68.9%prior 623
Gravel94 (2.2%)
↑3.3%prior 91
Mud, dirt3 (0.1%)
↓-62.5%prior 8
Other (explain in narrative)2 (0.0%)
Sand1 (0.0%)
Water (standing or moving)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Toyota leading in both periods. The rankings of the top three makes did not change year-over-year. An analysis of persons involved shows a shift in age representation; the proportion of individuals in the 16-20 age group increased from 12.9% to 14.7% of the total, and the 65+ age group's share grew from 12.3% to 14.8%.

Top Vehicle Makes (8,718 vehicles)

1
FORD1,406 (16.1%)
↑8.0%prior 1,302
2
CHEV1,130 (13%)
↓-1.1%prior 1,143
3
TOYT470 (5.4%)
↑9.0%prior 431
4
CHEVROLET417 (4.8%)
↓-2.3%prior 427
5
JEEP402 (4.6%)
↑6.9%prior 376
6
HOND312 (3.6%)
↓-14.3%prior 364
7
NISS304 (3.5%)
↑3.8%prior 293
8
DODG298 (3.4%)
↓-11.0%prior 335
9
GMC294 (3.4%)
↓-2.6%prior 302
10
KIA261 (3%)
↑11.5%prior 234

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · Vehicle unit records

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

Sex Distribution (5,605 persons with recorded sex)

Male3,159 (56.4%)
↓-28.2%prior 4,397
Female2,446 (43.6%)
↓-26.2%prior 3,316

Source: Iowa Crash Data · ArcGIS Open Data · 2024-10-01 to 2024-10-31 · 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: 2024-10-01 through 2024-10-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2024-10-01 through 2024-10-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 5,119
  • Total persons involved: 9,043
  • Total vehicles involved: 8,718

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: October 2024." Published September 9, 2026. Reporting period: 2024-10-01 to 2024-10-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/october-2024-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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