Yearly Traffic Safety Analysis

96 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In 2023, Lucas County recorded 96 total crashes, a 20.7% decrease from the 121 crashes documented in 2022. While total collisions declined, the number of fatalities remained unchanged at one. The most significant shift was the decrease in overall crash volume, accompanied by a corresponding 23.7% reduction in injuries from 38 to 29.

96

▼ -20.7%was 121

Total Crash Events

1

Persons Killed

29

▼ -23.7%was 38

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 crashes in Lucas County showed a downward trend year-over-year, with total incidents falling from 121 in 2022 to 96 in 2023. This represents a 20.7% decrease in crash volume. The number of people injured also declined by 23.7%, from 38 to 29, while fatalities held steady at one for both years.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Cyclists Injured

Prior: 0→%

28

Motorists Injured

Prior: 38↓-26.3%

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 shifted between the two periods. In 2023, the highest number of crashes occurred on Sundays, Mondays, and Thursdays, each with 15 incidents, and the peak hour was 5 p.m. with 8 crashes. This contrasts with 2022, when Tuesday was the peak day with 24 crashes and 2 p.m. was the peak hour with 13 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

Year-over-year, the number of fatal crashes remained stable at one, though the fatal crash rate increased slightly from 0.83% to 1.04% due to the lower total number of crashes. The proportion of crashes resulting in serious injuries decreased from 5.0% in 2022 to 4.2% in 2023. Conversely, the share of minor injury crashes rose from 7.4% to 9.4%, while possible injury crashes decreased from 14.9% to 11.5% of all incidents.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1%
→0.0%prior 1
Serious Injury4serious injury crashes4.2%
↓-33.3%prior 6
Minor Injury9minor injury crashes9.4%
→0.0%prior 9
Possible Injury11possible injury crashes11.5%
↓-38.9%prior 18
No Injury71no injury crashes74%
↓-18.4%prior 87

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

In both 2023 and 2022, collisions with animals were the leading contributing factor, with the count increasing slightly from 33 to 35. The count of crashes attributed to 'Lost Control' decreased from 13 to 8. Notably, incidents where a driver failed to yield the right-of-way from a stop sign more than doubled, increasing from 3 crashes in 2022 to 7 in 2023.

Officer-Reported Primary Contributing Cause

Animal35 (36.5%)↑6.1%prior 33
Other (explain in narrative): Other8 (8.3%)↓-27.3%prior 11
Lost Control8 (8.3%)↓-38.5%prior 13
FTYROW: From stop sign7 (7.3%)
FTYROW: Making left turn4 (4.2%)
Ran off road - straight3 (3.1%)↓-62.5%prior 8
Followed too close2 (2.1%)
Ran off road - left2 (2.1%)↓-66.7%prior 6
FTYROW: From driveway2 (2.1%)
Driver Distraction: Exterior distraction2 (2.1%)

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 on dry road surfaces were more prevalent in 2023, accounting for 86.6% of incidents with known road conditions, compared to 70.2% in 2022. Correspondingly, crashes on wet roads saw a significant drop from 14 to 3. The proportion of crashes occurring in daylight decreased from 69.1% in 2022 to 63.2% in 2023. Weather conditions remained broadly similar, with clear weather being the dominant condition in both years.

Weather

Clear51 (77.3%)
↓-27.1%prior 70
Cloudy11 (16.7%)
↑37.5%prior 8
Rain3 (4.5%)
↓-72.7%prior 11
Other (explain in narrative)1 (1.5%)

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

Lighting

Daylight43 (63.2%)
↓-33.8%prior 65
Dark - roadway not lighted12 (17.6%)
↓-29.4%prior 17
Dawn5 (7.4%)
Dark - roadway lighted5 (7.4%)
↓-28.6%prior 7
Dusk2 (2.9%)
Dark - unknown roadway lighting1 (1.5%)

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

Road Surface

Dry58 (86.6%)
↓-12.1%prior 66
Wet3 (4.5%)
↓-78.6%prior 14
Gravel2 (3.0%)
↓-66.7%prior 6
Snow2 (3.0%)
Ice/frost1 (1.5%)
↓-83.3%prior 6
Water (standing or moving)1 (1.5%)

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

Vehicles & Demographics

Ford and Chevrolet remained the top two vehicle makes involved in crashes in both periods, though the count for each decreased in 2023. The number of Fords involved fell from 40 to 23, and Chevrolets from 39 to 26. Looking at persons involved, there was a notable decrease in the 16-20 age group, from 27 individuals in 2022 to 17 in 2023. The number of males involved in crashes also saw a significant drop from 103 to 66, while the count for females was stable.

Top Vehicle Makes (139 vehicles)

1
FORD23 (16.5%)
↓-42.5%prior 40
2
CHEV17 (12.2%)
↓-46.9%prior 32
3
CHEVROLET9 (6.5%)
↑28.6%prior 7
4
DODG7 (5%)
↓-12.5%prior 8
5
NISS5 (3.6%)
→0.0%prior 5
6
JEEP5 (3.6%)
↓-50.0%prior 10
7
PONT4 (2.9%)
↓-20.0%prior 5
8
CHRY4 (2.9%)
9
RAM4 (2.9%)
10
GMC4 (2.9%)
↓-50.0%prior 8

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

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

Sex Distribution (125 persons with recorded sex)

Male66 (52.8%)
↓-35.9%prior 103
Female59 (47.2%)
↑9.3%prior 54

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: 96
  • Total persons involved: 204
  • Total vehicles involved: 139

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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