Yearly Traffic Safety Analysis

700 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Warren County, a total of 700 vehicle crashes were recorded in 2024, a 4.8% decrease from the 735 crashes reported in 2023. While total incidents declined, the most notable year-over-year shift was an 18.8% reduction in the number of people injured, which fell from 239 to 194. The number of fatalities remained unchanged at four.

700

▼ -4.8%was 735

Total Crash Events

Persons Killed

194

▼ -18.8%was 239

Persons Injured

▲ 33.3%was 3

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

Traffic crashes in Warren County showed a downward trend year-over-year. Total collisions fell by 35, from 735 in 2023 to 700 in 2024, representing a 4.8% decrease. This reduction was accompanied by a significant 18.8% drop in total injuries from 239 to 194, while the number of fatalities held steady at four for both periods.

Vulnerable Road User Casualties

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 4→0.0%

Cyclists Injured

Prior: 5→0.0%

189

Motorists Injured

Prior: 231↓-18.2%

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 peak day for crashes shifted from Thursday in 2023 (124 crashes) to Wednesday in 2024 (125 crashes). The 5 PM hour was a consistent peak time for collisions in both periods, accounting for 60 crashes in 2023 and 63 in 2024. Notably, the number of crashes on Wednesdays increased from 112 to 125 year-over-year, while Thursday crashes saw a significant drop from 124 to 88.

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 overall crashes decreased, the severity of crashes that did occur trended slightly higher. The number of fatal crashes increased from 3 to 4, and the count of serious injury crashes rose from 15 to 20. Consequently, the proportion of crashes involving possible or minor injuries declined, falling from a combined 24.2% of all crashes in 2023 to 20.0% in 2024. The share of non-injury crashes increased from 73.3% to 76.6%.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.6%

↑33.3%prior 3

Serious Injury20serious injury crashes2.9%

↑33.3%prior 15

Minor Injury65minor injury crashes9.3%

↓-14.5%prior 76

Possible Injury75possible injury crashes10.7%

↓-26.5%prior 102

No Injury536no injury crashes76.6%

↓-0.6%prior 539

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 animals remained the leading contributing factor in both periods, though the count of these incidents decreased by 20.1% from 179 in 2023 to 143 in 2024. 'Followed too close' became a more frequent cause, with its incident count rising 34.9% from 43 to 58, making it the second-most cited factor in 2024. Crashes attributed to 'Driving too fast for conditions' also saw a substantial increase, rising from 19 to 37 incidents.

Officer-Reported Primary Contributing Cause

Animal143 (20.4%)↓-20.1%prior 179

Followed too close58 (8.3%)↑34.9%prior 43

Other (explain in narrative): Other57 (8.1%)↓-19.7%prior 71

Driving too fast for conditions37 (5.3%)↑94.7%prior 19

FTYROW: From stop sign35 (5%)↓-23.9%prior 46

Lost Control32 (4.6%)↑23.1%prior 26

Ran off road - left30 (4.3%)↓-23.1%prior 39

Ran off road - straight29 (4.1%)↓-27.5%prior 40

FTYROW: Making left turn23 (3.3%)↓-36.1%prior 36

Driver Distraction: Other interior distraction19 (2.7%)↑72.7%prior 11

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 distribution of crashes across various environmental conditions remained largely consistent year-over-year. Crashes in daylight accounted for 58.3% of all incidents in 2024, a marginal increase from 56.6% in 2023, while collisions in clear weather held steady at approximately 61% in both periods. There was a slight increase in the proportion of crashes occurring on adverse road surfaces like wet, icy, or snowy roads, which grew from 12.7% of all crashes in 2023 to 15.3% in 2024.

Weather

Clear426 (73.8%)

↓-6.0%prior 453

Cloudy91 (15.8%)

↑7.1%prior 85

Rain25 (4.3%)

↓-7.4%prior 27

Snow13 (2.3%)

↓-45.8%prior 24

Freezing rain/drizzle8 (1.4%)

↑60.0%prior 5

Blowing Snow7 (1.2%)

Severe Winds3 (0.5%)

Fog, smoke, smog3 (0.5%)

↓-40.0%prior 5

Other (explain in narrative)1 (0.2%)

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

Lighting

Daylight408 (70.1%)

↓-1.9%prior 416

Dark - roadway not lighted72 (12.4%)

↓-22.6%prior 93

Dark - roadway lighted57 (9.8%)

↑9.6%prior 52

Dawn19 (3.3%)

↑11.8%prior 17

Dusk19 (3.3%)

↓-9.5%prior 21

Dark - unknown roadway lighting7 (1.2%)

↑16.7%prior 6

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

Road Surface

Dry460 (79.6%)

↓-4.2%prior 480

Wet47 (8.1%)

↓-7.8%prior 51

Ice/frost26 (4.5%)

↑85.7%prior 14

Snow25 (4.3%)

↑8.7%prior 23

Gravel11 (1.9%)

↓-62.1%prior 29

Slush6 (1.0%)

Mud, dirt3 (0.5%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes were highly consistent year-over-year, with Ford (188 vehicles) and Chevrolet (181 vehicles) leading in 2024, similar to the prior year. The number of Jeeps in crashes increased from 56 to 71, moving it into the third-highest rank. The proportional age distribution of persons involved in crashes also remained stable; for instance, the 16-20 age group accounted for approximately 14% of individuals in both periods.

Top Vehicle Makes (1,146 vehicles)

FORD188 (16.4%)

↑2.2%prior 184

CHEV181 (15.8%)

→0.0%prior 181

JEEP71 (6.2%)

↑26.8%prior 56

TOYT63 (5.5%)

↓-8.7%prior 69

HOND48 (4.2%)

↓-2.0%prior 49

NISS48 (4.2%)

↑17.1%prior 41

DODG47 (4.1%)

↓-23.0%prior 61

CHEVROLET45 (3.9%)

↓-6.3%prior 48

GMC38 (3.3%)

↓-13.6%prior 44

HYUN32 (2.8%)

↑68.4%prior 19

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

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

Sex Distribution (748 persons with recorded sex)

Male452 (60.4%)

↓-27.1%prior 620

Female296 (39.6%)

↓-36.1%prior 463

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: 700
Total persons involved: 1,181
Total vehicles involved: 1,146

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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700 CRASHES INIOWA, IA2024