Yearly Traffic Safety Analysis

735 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Warren County, total crashes increased from 701 in 2022 to 735 in 2023, a rise of approximately 4.9%. While the number of fatalities resulting from these crashes fell from 6 to 4, the total number of people injured grew by 14.4% from 209 to 239. The most significant shifts were a decrease in serious injury crashes and an increase in crashes involving minor or possible injuries.

735

▲ 4.9%was 701

Total Crash Events

4

▼ -33.3%was 6

Persons Killed

239

▲ 14.4%was 209

Persons Injured

3

▼ -40.0%was 5

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Warren County indicates a rising trend in collisions in 2023 compared to the previous year. Total crashes increased by 4.9%, from 701 to 735. This was accompanied by a 14.4% increase in the number of people injured, while the number of fatalities decreased from 6 to 4.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 6↓-33.3%

3

Pedestrians Injured

Prior: 1↑200.0%

5

Cyclists Injured

Prior: 4↑25.0%

231

Motorists Injured

Prior: 204↑13.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes in Warren County showed some shifts between 2022 and 2023. The peak day for crashes moved from Friday (119 incidents) in 2022 to Thursday (124 incidents) in 2023. The 5 p.m. hour was a peak time in both years, though 2023 also saw peaks at 7 a.m. and 3 p.m., each with 60 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shifted between the two periods. The fatal crash rate decreased from 0.71 per 100 crashes in 2022 to 0.41 in 2023, with the count of fatal incidents dropping from 5 to 3. While the count of serious injury crashes fell from 27 to 15, crashes resulting in minor or possible injuries increased. Minor injury crashes rose from 68 to 76, and possible injury crashes grew from 78 to 102.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.4%
↓-40.0%prior 5
Serious Injury15serious injury crashes2%
↓-44.4%prior 27
Minor Injury76minor injury crashes10.3%
↑11.8%prior 68
Possible Injury102possible injury crashes13.9%
↑30.8%prior 78
No Injury539no injury crashes73.3%
↑3.1%prior 523

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both 2022 (180 crashes) and 2023 (179 crashes), accounting for roughly a quarter of all incidents each year. Notably, crashes attributed to 'Failure to Yield Right of Way from a stop sign' increased by 31.4% in count, from 35 incidents in 2022 to 46 in 2023. Conversely, crashes where a driver lost control saw a significant 44.7% decrease in count, dropping from 47 to 26.

Officer-Reported Primary Contributing Cause

Animal179 (24.4%)↓-0.6%prior 180
Other (explain in narrative): Other71 (9.7%)↑42.0%prior 50
FTYROW: From stop sign46 (6.3%)↑31.4%prior 35
Followed too close43 (5.9%)↑16.2%prior 37
Ran off road - straight40 (5.4%)↑2.6%prior 39
Ran off road - left39 (5.3%)↓-4.9%prior 41
FTYROW: Making left turn36 (4.9%)↑16.1%prior 31
Lost Control26 (3.5%)↓-44.7%prior 47
Driving too fast for conditions19 (2.6%)↓-17.4%prior 23
Operating vehicle in an reckless, erratic, careless, negligent manner15 (2%)↑50.0%prior 10

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in clear weather and on dry roads constituted a larger share of total incidents in 2023 compared to 2022. Collisions during daylight hours increased from 370 to 416, representing 56.6% of all crashes in 2023 versus 52.8% in the prior year. Similarly, crashes on dry road surfaces rose from 417 to 480. Incidents occurring in adverse conditions like snow or on wet roads saw a slight decrease in both count and proportion.

Weather

Clear453 (75.1%)
↑17.4%prior 386
Cloudy85 (14.1%)
↓-12.4%prior 97
Rain27 (4.5%)
↓-6.9%prior 29
Snow24 (4.0%)
↓-11.1%prior 27
Freezing rain/drizzle5 (0.8%)
↓-44.4%prior 9
Fog, smoke, smog5 (0.8%)
→0.0%prior 5
Severe Winds4 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight416 (68.8%)
↑12.4%prior 370
Dark - roadway not lighted93 (15.4%)
↓-11.4%prior 105
Dark - roadway lighted52 (8.6%)
↑15.6%prior 45
Dusk21 (3.5%)
→0.0%prior 21
Dawn17 (2.8%)
↓-34.6%prior 26
Dark - unknown roadway lighting6 (1.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry480 (79.6%)
↑15.1%prior 417
Wet51 (8.5%)
↓-8.9%prior 56
Gravel29 (4.8%)
→0.0%prior 29
Snow23 (3.8%)
↓-28.1%prior 32
Ice/frost14 (2.3%)
↓-46.2%prior 26
Slush4 (0.7%)
↓-50.0%prior 8
Mud, dirt1 (0.2%)
Other (explain in narrative)1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford and Chevrolet being the top two in both 2022 and 2023. An analysis of persons involved in crashes shows a notable shift in age distribution, with a substantial increase in individuals from older age groups. The number of people in the 35-44 age group grew from 209 to 254, the 55-64 group increased from 159 to 212, and the 65+ group rose from 154 to 206.

Top Vehicle Makes (1,169 vehicles)

1
FORD184 (15.7%)
↑7.0%prior 172
2
CHEV181 (15.5%)
↑13.1%prior 160
3
TOYT69 (5.9%)
↑23.2%prior 56
4
DODG61 (5.2%)
↑19.6%prior 51
5
JEEP56 (4.8%)
↓-5.1%prior 59
6
HOND49 (4.2%)
↑44.1%prior 34
7
CHEVROLET48 (4.1%)
↓-2.0%prior 49
8
GMC44 (3.8%)
↓-8.3%prior 48
9
NISS41 (3.5%)
↑24.2%prior 33
10
CHRY34 (2.9%)
↑61.9%prior 21

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (1,083 persons with recorded sex)

Male620 (57.2%)
↑5.3%prior 589
Female463 (42.8%)
↑16.9%prior 396

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 735
  • Total persons involved: 1,618
  • Total vehicles involved: 1,169

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: 2023." Published September 9, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2023-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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