Yearly Traffic Safety Analysis

209 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Carroll County recorded 209 total crashes, a 12.6% decrease from the 239 crashes reported in 2019. Despite the overall reduction in collisions, the number of fatalities increased from one in 2019 to three in 2020. The total number of injuries saw a decrease from 101 to 75 over the same period.

209

▼ -12.6%was 239

Total Crash Events

3

▲ 200.0%was 1

Persons Killed

75

▼ -25.7%was 101

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (3) 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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in traffic crashes in Carroll County shows a year-over-year decline. Total crashes fell by 12.6%, from 239 in 2019 to 209 in 2020, and total injuries decreased by 25.7%. However, this downward trend did not extend to the most severe outcomes, as fatalities rose from one to three during the same period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 1↑200.0%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

71

Motorists Injured

Prior: 101↓-29.7%

1

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal patterns of crashes shifted between 2019 and 2020. The peak day for collisions moved from Tuesday (41 crashes) in 2019 to a tie between Thursday and Friday (38 crashes each) in 2020. While the 3 p.m. hour remained a consistent peak time for crashes in both years, the number of crashes during that hour decreased from 23 to 17. Crashes on weekends (Saturday and Sunday) saw a notable reduction, falling from 63 in 2019 to 33 in 2020.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the severity of crashes worsened year-over-year. The number of fatal crashes tripled from one in 2019 to three in 2020, increasing the fatal crash rate from 0.4% to 1.4% of all crashes. The count of serious injury crashes also rose slightly from four to five. Conversely, crashes resulting in minor or possible injuries declined from 72 in 2019 to 55 in 2020.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.4%
↑200.0%prior 1
Serious Injury5serious injury crashes2.4%
↑25.0%prior 4
Minor Injury27minor injury crashes12.9%
↓-34.1%prior 41
Possible Injury28possible injury crashes13.4%
↓-9.7%prior 31
No Injury146no injury crashes69.9%
↓-9.9%prior 162

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

Failure to yield the right of way from a stop sign was the leading contributing factor in both periods, accounting for 20 crashes in both 2019 and 2020. While the top factor's count remained stable, there were shifts in other categories; crashes attributed to 'Ran Stop Sign' increased from 14 to 18. Notably, incidents involving 'Ran Traffic Signal' more than doubled, rising from 4 to 10. Conversely, crashes related to 'Ran off road - left' and 'Driving too fast for conditions' saw significant decreases, falling from 19 to 11 and 16 to 9, respectively.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign20 (9.6%)→0.0%prior 20
Ran Stop Sign18 (8.6%)↑28.6%prior 14
Followed too close13 (6.2%)↓-18.8%prior 16
Lost Control13 (6.2%)↑18.2%prior 11
Other (explain in narrative): Other12 (5.7%)↓-7.7%prior 13
Ran off road - left11 (5.3%)↓-42.1%prior 19
Animal10 (4.8%)→0.0%prior 10
Ran Traffic Signal10 (4.8%)
FTYROW: Making left turn9 (4.3%)↓-30.8%prior 13
Driving too fast for conditions9 (4.3%)↓-43.8%prior 16

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes were predominantly reported in clear weather and on dry roads in both 2019 and 2020. However, there was a notable year-over-year decrease in collisions occurring under adverse conditions. Crashes on wet, snowy, or icy roads dropped from 79 in 2019 to 45 in 2020. Similarly, incidents in dark conditions fell from 57 to 36, and the proportion of crashes happening in daylight increased from 69.9% to 71.3%.

Weather

Clear157 (78.5%)
↑1.3%prior 155
Cloudy19 (9.5%)
↓-61.2%prior 49
Snow8 (4.0%)
↓-20.0%prior 10
Rain6 (3.0%)
↓-45.5%prior 11
Fog, smoke, smog5 (2.5%)
Blowing Snow3 (1.5%)
Freezing rain/drizzle2 (1.0%)
↓-66.7%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight149 (74.5%)
↓-10.8%prior 167
Dark - roadway lighted19 (9.5%)
↓-26.9%prior 26
Dark - roadway not lighted15 (7.5%)
↓-50.0%prior 30
Dusk12 (6.0%)
↑71.4%prior 7
Dawn3 (1.5%)
↓-50.0%prior 6
Dark - unknown roadway lighting2 (1.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry155 (77.5%)
↓-1.9%prior 158
Wet19 (9.5%)
↓-20.8%prior 24
Snow10 (5.0%)
↓-56.5%prior 23
Gravel10 (5.0%)
↑11.1%prior 9
Ice/frost5 (2.5%)
↓-75.0%prior 20
Slush1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Chevrolet and Ford ranking first and second in both years, though the number of vehicles from both makes involved in crashes decreased. An analysis of person demographics shows a shift in age group involvement; the 16-20 age group saw an increase in their absolute count from 87 to 91 people involved in crashes. Conversely, the number of persons aged 65 and older involved in collisions decreased from 91 to 70.

Top Vehicle Makes (367 vehicles)

1
FORD67 (18.3%)
↓-13.0%prior 77
2
CHEV67 (18.3%)
↓-8.2%prior 73
3
CHEVROLET32 (8.7%)
↑10.3%prior 29
4
DODG20 (5.4%)
↑11.1%prior 18
5
GMC18 (4.9%)
→0.0%prior 18
6
CHRY13 (3.5%)
↓-7.1%prior 14
7
JEEP11 (3%)
↓-15.4%prior 13
8
PONT10 (2.7%)
↓-9.1%prior 11
9
BUIC9 (2.5%)
↓-43.8%prior 16
10
TOYO9 (2.5%)
↓-25.0%prior 12

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (328 persons with recorded sex)

Male192 (58.5%)
↓-7.2%prior 207
Female136 (41.5%)
↓-26.5%prior 185

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 209
  • Total persons involved: 489
  • Total vehicles involved: 367

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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