Yearly Traffic Safety Analysis

282 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In 2023, Buchanan County recorded 282 total crashes, a 7.2% increase from the 263 crashes reported in 2022. This rise was accompanied by a more significant 65.2% increase in total injuries, which grew from 66 in the prior year to 109 in the current period. Fatalities also increased from one to two year-over-year.

282

▲ 7.2%was 263

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

109

▲ 65.2%was 66

Persons Injured

2

▲ 100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 Buchanan County shows an upward trend year-over-year. Total crashes increased by 7.2%, from 263 in 2022 to 282 in 2023. This trend extended to crash severity, with total injuries rising by 65.2% (from 66 to 109) and fatalities doubling from one to two.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 1↑100.0%

109

Motorists Injured

Prior: 64↑70.3%

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 showed a distinct shift between the two periods. The peak day for crashes moved from Saturday (49 crashes) in 2022 to Thursday (53 crashes) in 2023. More notably, the peak hour for collisions shifted from the evening at 6 p.m. (19 crashes) in the prior year to the morning commute at 7 a.m. (22 crashes) in the current year.

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 worsened in 2023 compared to 2022, with the fatal crash rate increasing from 0.38 to 0.71 per 100 crashes. The proportion of crashes resulting in any injury rose from 19.8% in 2022 to 29.1% in 2023. This was driven by a large increase in minor injury crashes, which grew from 19 incidents to 40, accounting for 14.2% of all crashes in the current period compared to 7.2% previously.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.7%
↑100.0%prior 1
Serious Injury8serious injury crashes2.8%
↑33.3%prior 6
Minor Injury40minor injury crashes14.2%
↑110.5%prior 19
Possible Injury34possible injury crashes12.1%
↑25.9%prior 27
No Injury198no injury crashes70.2%
↓-5.7%prior 210

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 with an animal remained the leading contributing factor in both periods, with counts increasing from 88 to 94. A significant year-over-year change was observed in crashes where a driver 'Ran off road - left,' which saw its count increase by 150% from 8 to 20. Crashes attributed to 'FTYROW: Making left turn' also more than doubled from 5 to 11, while incidents involving 'Driving too fast for conditions' decreased from 17 to 13.

Officer-Reported Primary Contributing Cause

Animal94 (33.3%)↑6.8%prior 88
Ran off road - left20 (7.1%)↑150.0%prior 8
Ran off road - straight17 (6%)↑13.3%prior 15
Lost Control17 (6%)↑13.3%prior 15
Followed too close14 (5%)↑16.7%prior 12
Other (explain in narrative): Other14 (5%)↑7.7%prior 13
Driving too fast for conditions13 (4.6%)↓-23.5%prior 17
FTYROW: Making left turn11 (3.9%)↑120.0%prior 5
Driver Distraction: Other interior distraction9 (3.2%)
Driver Distraction: Inattentive/lost in thought8 (2.8%)

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

Road & Environmental Conditions

While the distribution of crashes by weather and road surface condition remained relatively stable, there was a notable increase in collisions occurring in darkness. The number of crashes on unlighted dark roadways grew from 36 in 2022 to 51 in 2023. Crashes in daylight conditions were identical in count at 127 for both years, but their share of the total decreased from 48.3% to 45.0%.

Weather

Clear144 (71.6%)
↑14.3%prior 126
Cloudy21 (10.4%)
↓-19.2%prior 26
Freezing rain/drizzle12 (6.0%)
↑140.0%prior 5
Snow10 (5.0%)
↓-23.1%prior 13
Rain7 (3.5%)
Blowing Snow3 (1.5%)
↓-57.1%prior 7
Fog, smoke, smog3 (1.5%)
Sleet, hail1 (0.5%)

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

Lighting

Daylight127 (62.9%)
→0.0%prior 127
Dark - roadway not lighted51 (25.2%)
↑41.7%prior 36
Dark - roadway lighted11 (5.4%)
↓-21.4%prior 14
Dawn7 (3.5%)
Dusk4 (2.0%)
Dark - unknown roadway lighting2 (1.0%)

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

Road Surface

Dry143 (71.5%)
↑11.7%prior 128
Ice/frost19 (9.5%)
↓-13.6%prior 22
Wet16 (8.0%)
↑77.8%prior 9
Snow9 (4.5%)
↓-47.1%prior 17
Gravel8 (4.0%)
↑33.3%prior 6
Slush4 (2.0%)
Mud, dirt1 (0.5%)

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

Vehicles & Demographics

The ranking of the most frequently involved vehicle makes shifted, with Chevrolet (75 vehicles) overtaking Ford (65 vehicles) for the top position in 2023; this reverses the order from 2022 when Ford led with 81 vehicles. An analysis of persons involved shows the count of individuals in the 0-15 age group nearly tripled from 8 to 23. The 65+ age group also saw an increase in representation, growing from 67 to 82 persons involved year-over-year.

Top Vehicle Makes (387 vehicles)

1
CHEV75 (19.4%)
↑10.3%prior 68
2
FORD65 (16.8%)
↓-19.8%prior 81
3
DODG26 (6.7%)
↑52.9%prior 17
4
JEEP16 (4.1%)
↑77.8%prior 9
5
KIA12 (3.1%)
↑50.0%prior 8
6
RAM12 (3.1%)
↑9.1%prior 11
7
TOYO12 (3.1%)
↑71.4%prior 7
8
GMC11 (2.8%)
↓-35.3%prior 17
9
HOND11 (2.8%)
↓-21.4%prior 14
10
TOYT11 (2.8%)

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

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

Sex Distribution (368 persons with recorded sex)

Male236 (64.1%)
↑9.3%prior 216
Female132 (35.9%)
↓-3.6%prior 137

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 10, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 282
  • Total persons involved: 590
  • Total vehicles involved: 387

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 10, 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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