Yearly Traffic Safety Analysis

210 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Louisa County, total vehicle crashes increased by 22.8% from 171 in 2022 to 210 in 2023. This rise was accompanied by increases in both injuries, which grew from 34 to 48, and fatalities, which saw the most significant year-over-year shift, increasing from 1 to 7.

210

▲ 22.8%was 171

Total Crash Events

7

▲ 600.0%was 1

Persons Killed

48

▲ 41.2%was 34

Persons Injured

5

▲ 400.0%was 1

Fatal Crash Events

Note: "Persons Killed" (7) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

Traffic safety trends in Louisa County worsened from 2022 to 2023. The total number of crashes rose from 171 to 210, a 22.8% increase. This upward trend was also reflected in crash severity, with total injuries rising by 41.2% and fatalities increasing by 600% year-over-year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

7

Motorists Killed

Prior: 1↑600.0%

1

Cyclists Injured

Prior: 0→%

47

Motorists Injured

Prior: 32↑46.9%

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 timing of crashes shifted between the two periods. In 2023, the peak day for crashes was Wednesday with 36 incidents, a change from 2022 when Tuesday and Friday were tied for the most crashes at 30 each. The peak hours for crashes also evolved from a single peak at 5 a.m. in 2022 (17 crashes) to a dual peak in 2023 at 6 a.m. and 5 p.m. (19 crashes each), aligning with commute times.

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

Crash severity increased significantly from 2022 to 2023. The number of fatal crashes rose from 1 to 5, increasing the fatal crash rate from 0.6% to 2.4% of all incidents. While the number of serious injury crashes remained stable at 3 in both years, the total number of individuals injured increased from 34 to 48. The proportion of crashes resulting in no injuries was consistent, at 81.9% in 2022 and 81.0% in 2023.

Severity is per crash event (most severe injury). 5 fatal crash events resulted in 7 persons killed.

Outcome by Severity (Crash Events)

Fatal5fatal crashes2.4%
↑400.0%prior 1
Serious Injury3serious injury crashes1.4%
→0.0%prior 3
Minor Injury20minor injury crashes9.5%
↑66.7%prior 12
Possible Injury12possible injury crashes5.7%
↓-20.0%prior 15
No Injury170no injury crashes81%
↑21.4%prior 140

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 years, with the count of these incidents increasing by 25% from 92 in 2022 to 115 in 2023. This factor's share of total crashes held steady at approximately 54%. Notably, crashes attributed to 'Failure to Yield Right of Way from a stop sign' saw a significant increase in count, from 1 to 7 incidents. Crashes where 'Exceeded authorized speed' was a factor also increased, rising from 1 to 5.

Officer-Reported Primary Contributing Cause

Animal115 (54.8%)↑25.0%prior 92
Lost Control9 (4.3%)↓-10.0%prior 10
Ran off road - straight9 (4.3%)↑50.0%prior 6
Ran off road - left8 (3.8%)→0.0%prior 8
FTYROW: From stop sign7 (3.3%)
Other (explain in narrative): Other7 (3.3%)↑16.7%prior 6
Exceeded authorized speed5 (2.4%)
Ran off road - right4 (1.9%)
Followed too close4 (1.9%)
Swerving/Evasive Action4 (1.9%)↓-20.0%prior 5

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

Road & Environmental Conditions

The conditions under which crashes occurred showed some shifts year-over-year. In 2023, a larger proportion of crashes happened in clear weather (74% vs. 68% in 2022) and on dry roads (78% vs. 70%). Conversely, the share of crashes occurring in daylight conditions decreased from 60% in 2022 to 53% in 2023, with a corresponding increase in crashes taking place in dark, unlit roadway conditions (34% in 2023 vs. 28% in 2022).

Weather

Clear87 (74.4%)
↑33.8%prior 65
Cloudy13 (11.1%)
↓-40.9%prior 22
Rain7 (6.0%)
Snow5 (4.3%)
Severe Winds2 (1.7%)
Other (explain in narrative)1 (0.9%)
Freezing rain/drizzle1 (0.9%)
Fog, smoke, smog1 (0.9%)

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

Lighting

Daylight61 (53.0%)
↑3.4%prior 59
Dark - roadway not lighted39 (33.9%)
↑44.4%prior 27
Dark - roadway lighted9 (7.8%)
↑12.5%prior 8
Dawn4 (3.5%)
Dusk2 (1.7%)

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

Road Surface

Dry93 (78.2%)
↑36.8%prior 68
Wet11 (9.2%)
↑22.2%prior 9
Gravel4 (3.4%)
↓-42.9%prior 7
Ice/frost4 (3.4%)
↓-42.9%prior 7
Snow3 (2.5%)
Slush2 (1.7%)
Mud, dirt2 (1.7%)

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

Vehicles & Demographics

While the top vehicle makes involved in crashes remained similar, their rankings shifted; Chevrolet (42 vehicles) surpassed Ford (40 vehicles) as the most common make in 2023, a reversal from 2022 when Ford led with 49 vehicles. Analysis of persons involved shows the 35-44 age group had the largest increase, growing from 56 individuals in 2022 to 83 in 2023. The 26-34 age group also saw a notable increase in involvement, from 45 to 71 persons.

Top Vehicle Makes (260 vehicles)

1
FORD40 (15.4%)
↓-18.4%prior 49
2
CHEV34 (13.1%)
→0.0%prior 34
3
CHRY16 (6.2%)
4
DODG14 (5.4%)
↓-39.1%prior 23
5
GMC12 (4.6%)
↑50.0%prior 8
6
JEEP11 (4.2%)
7
TOYT10 (3.8%)
↑25.0%prior 8
8
NISS10 (3.8%)
9
DODGE10 (3.8%)
↑25.0%prior 8
10
HOND9 (3.5%)
↑12.5%prior 8

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

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

Sex Distribution (249 persons with recorded sex)

Male156 (62.7%)
↑23.8%prior 126
Female93 (37.3%)
↑14.8%prior 81

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: 210
  • Total persons involved: 410
  • Total vehicles involved: 260

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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