Yearly Traffic Safety Analysis

76 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Van Buren County, total crashes increased from 62 in 2022 to 76 in 2023, a 22.6% rise. Despite the increase in overall collisions, the number of fatalities fell from two in the prior period to zero in the current period. The total number of injuries also saw an increase from 31 to 36.

76

▲ 22.6%was 62

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

36

▲ 16.1%was 31

Persons Injured

0

▼ -100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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 trends in Van Buren County showed an upward movement year-over-year. Total crashes rose by 22.6%, from 62 in 2022 to 76 in 2023. This was accompanied by a 16.1% increase in total injuries, which grew from 31 to 36 over the same period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 2↓-100.0%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 1→0.0%

33

Motorists Injured

Prior: 30↑10.0%

1

Other Injured

Prior: 0→%

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. The peak day for crashes moved from Saturday (18 crashes) in 2022 to Thursday (14 crashes) in 2023. Similarly, the peak hour for collisions changed from 11 a.m. in the prior year (8 crashes) to 7 a.m. in the current year (7 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

Crash severity improved year-over-year, with fatal crashes decreasing from two in 2022 to zero in 2023. The proportion of crashes resulting in serious injuries increased from 6.5% to 10.5% of all incidents. Conversely, crashes with minor or possible injuries saw a combined decrease in their share, while no-injury crashes rose from 54.8% to 59.2% of the total.

Outcome by Severity (Crash Events)

Serious Injury8serious injury crashes10.5%
↑100.0%prior 4
Minor Injury13minor injury crashes17.1%
↑8.3%prior 12
Possible Injury10possible injury crashes13.2%
→0.0%prior 10
No Injury45no injury crashes59.2%
↑32.4%prior 34

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

The top contributing factors remained consistent, though their counts increased. Collisions involving an 'Animal' became the top-ranked factor, with the count increasing by 77.8% from 9 to 16 incidents. 'Lost Control' incidents also increased from 12 to 15 crashes, a 25% rise in count. Crashes attributed to 'Ran off road - straight' grew from 6 to 9 incidents.

Officer-Reported Primary Contributing Cause

Animal16 (21.1%)↑77.8%prior 9
Lost Control15 (19.7%)↑25.0%prior 12
Ran off road - straight9 (11.8%)↑50.0%prior 6
Driving too fast for conditions5 (6.6%)
Ran off road - left3 (3.9%)
Ran Stop Sign3 (3.9%)
Followed too close2 (2.6%)
FTYROW: Making left turn2 (2.6%)
Other (explain in narrative): No improper action2 (2.6%)
Other (explain in narrative): Other2 (2.6%)

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 proportion of crashes occurring in daylight decreased from 66.1% in 2022 to 51.3% in 2023, while crashes on unlit dark roadways increased their share from 21.0% to 31.6% of the total. Crashes on dry road surfaces remained the majority in both years, constituting 69.7% of incidents in 2023 compared to 66.1% in 2022. The share of crashes in clear weather conditions increased slightly from 61.3% to 67.1%.

Weather

Clear51 (73.9%)
↑34.2%prior 38
Cloudy11 (15.9%)
↓-26.7%prior 15
Snow4 (5.8%)
Freezing rain/drizzle1 (1.4%)
Other (explain in narrative)1 (1.4%)
Rain1 (1.4%)

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

Lighting

Daylight39 (56.5%)
↓-4.9%prior 41
Dark - roadway not lighted24 (34.8%)
↑84.6%prior 13
Dawn4 (5.8%)
Dark - roadway lighted1 (1.4%)
Dusk1 (1.4%)

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

Road Surface

Dry53 (76.8%)
↑29.3%prior 41
Slush5 (7.2%)
Wet5 (7.2%)
↓-37.5%prior 8
Gravel3 (4.3%)
Snow2 (2.9%)
Mud, dirt1 (1.4%)

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, with Ford vehicles increasing from 20 to 22 and Chevrolet vehicles decreasing from 13 to 10. The demographic profile of persons involved in crashes showed a shift toward younger individuals. The number of persons in the 16-20 age group increased from 18 to 28, and the 21-25 age group's involvement grew from 14 to 21 persons.

Top Vehicle Makes (95 vehicles)

1
FORD22 (23.2%)
↑10.0%prior 20
2
CHEV10 (10.5%)
↓-23.1%prior 13
3
DODG6 (6.3%)
↓-14.3%prior 7
4
GMC6 (6.3%)
5
DODGE5 (5.3%)
6
JEEP4 (4.2%)
7
INTERNATIONA3 (3.2%)
8
CHEVROLET3 (3.2%)
9
HOND3 (3.2%)
10
LINCOLN2 (2.1%)

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

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

Sex Distribution (93 persons with recorded sex)

Male62 (66.7%)
↑21.6%prior 51
Female31 (33.3%)
↓-8.8%prior 34

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: 76
  • Total persons involved: 144
  • Total vehicles involved: 95

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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