Yearly Traffic Safety Analysis

228 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In Carroll County, total vehicle crashes increased by 9.1% from 209 in 2020 to 228 in 2021. While overall crashes and injuries (from 75 to 79) saw a slight rise, the most significant year-over-year change was the complete elimination of fatal crashes, which dropped from 3 in the prior year to 0 in the current year.

228

▲ 9.1%was 209

Total Crash Events

0

▼ -100.0%was 3

Persons Killed

79

▲ 5.3%was 75

Persons Injured

0

▼ -100.0%was 3

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 · 2021-01-01 to 2021-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Carroll County showed a moderate increase year-over-year, with total incidents rising from 209 to 228. This represents a 9.1% increase in crash volume. However, this increase in crashes was accompanied by a positive trend in severity, as the number of persons killed in crashes fell from 3 in 2020 to 0 in 2021.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 3↓-100.0%

0

Other Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 1→0.0%

77

Motorists Injured

Prior: 71↑8.5%

1

Other Injured

Prior: 1→0.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained broadly consistent year-over-year. Friday continued to be the peak day for crashes, with the count increasing from 38 in 2020 to 47 in 2021. The afternoon commute hour of 3 p.m. also remained a peak time, with crashes during this hour increasing from 17 to 21. The overall distribution of crashes throughout the week and day did not show a major shift in pattern, but rather an amplification of existing peak times.

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

A significant improvement in crash severity was observed between the two periods. The number of fatal crashes dropped from 3 in 2020 to 0 in 2021, and consequently, the number of fatalities also dropped from 3 to 0. The number of serious injury crashes remained stable at 5 incidents in both years. The proportion of crashes resulting in no injuries increased slightly, from 69.9% (146 crashes) in 2020 to 71.5% (163 crashes) in 2021.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes2.2%
→0.0%prior 5
Minor Injury30minor injury crashes13.2%
↑11.1%prior 27
Possible Injury30possible injury crashes13.2%
↑7.1%prior 28
No Injury163no injury crashes71.5%
↑11.6%prior 146

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Most severe injury per crash record

Top Contributing Factors

The ranking of top contributing factors shifted between 2020 and 2021. In 2021, "Followed too close" became the leading factor with 22 crashes, a notable increase from its count of 13 in the prior year. Crashes attributed to "Lost Control" also rose from 13 to 19. Conversely, factors related to stop signs saw a decrease; crashes from "FTYROW: From stop sign" fell from 20 to 17, and crashes from "Ran Stop Sign" were halved, dropping from 18 to 9.

Officer-Reported Primary Contributing Cause

Followed too close22 (9.6%)↑69.2%prior 13
Lost Control19 (8.3%)↑46.2%prior 13
FTYROW: From stop sign17 (7.5%)↓-15.0%prior 20
Other (explain in narrative): Other16 (7%)↑33.3%prior 12
Driver Distraction: Other interior distraction16 (7%)↑166.7%prior 6
Animal15 (6.6%)↑50.0%prior 10
Improper Backing12 (5.3%)↑71.4%prior 7
Driving too fast for conditions11 (4.8%)↑22.2%prior 9
Ran Stop Sign9 (3.9%)↓-50.0%prior 18
Ran off road - left7 (3.1%)↓-36.4%prior 11

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While most crashes in both years occurred in clear weather on dry roads during daylight, there was a notable shift in crashes under adverse lighting. The number of crashes in "Dark - roadway not lighted" conditions more than doubled, increasing from 15 in 2020 to 36 in 2021. Crashes in rainy conditions also increased from 6 to 10. Correspondingly, the share of crashes during daylight hours decreased from 71.3% in 2020 to 65.8% in 2021.

Weather

Clear163 (73.8%)
↑3.8%prior 157
Cloudy33 (14.9%)
↑73.7%prior 19
Rain10 (4.5%)
↑66.7%prior 6
Snow8 (3.6%)
→0.0%prior 8
Severe Winds2 (0.9%)
Fog, smoke, smog2 (0.9%)
↓-60.0%prior 5
Freezing rain/drizzle2 (0.9%)
Blowing Snow1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Weather condition at time of crash

Lighting

Daylight150 (67.6%)
↑0.7%prior 149
Dark - roadway not lighted36 (16.2%)
↑140.0%prior 15
Dark - roadway lighted21 (9.5%)
↑10.5%prior 19
Dawn7 (3.2%)
Dusk5 (2.3%)
↓-58.3%prior 12
Dark - unknown roadway lighting3 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Lighting condition field

Road Surface

Dry159 (71.9%)
↑2.6%prior 155
Wet21 (9.5%)
↑10.5%prior 19
Gravel15 (6.8%)
↑50.0%prior 10
Snow14 (6.3%)
↑40.0%prior 10
Ice/frost10 (4.5%)
↑100.0%prior 5
Slush1 (0.5%)
Other (explain in narrative)1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Road surface condition field

Vehicles & Demographics

Vehicle make involvement showed some shifts between periods. While Ford and Chevrolet remained the top two makes involved in crashes, the combined count for Chevrolet vehicles ('Chev' and 'Chevrolet') increased from 99 in 2020 to 114 in 2021. The number of Jeeps involved in crashes nearly doubled, from 11 to 21. Regarding the age of persons involved, there was a decrease in the 16-20 age group (from 91 to 80 persons) and the 65+ age group (from 70 to 56 persons), while the 35-44 age group saw an increase from 43 to 60 persons.

Top Vehicle Makes (384 vehicles)

1
FORD66 (17.2%)
↓-1.5%prior 67
2
CHEV59 (15.4%)
↓-11.9%prior 67
3
CHEVROLET55 (14.3%)
↑71.9%prior 32
4
JEEP21 (5.5%)
↑90.9%prior 11
5
DODGE14 (3.6%)
↑133.3%prior 6
6
TOYO14 (3.6%)
↑55.6%prior 9
7
GMC13 (3.4%)
↓-27.8%prior 18
8
CHRY12 (3.1%)
↓-7.7%prior 13
9
PONT12 (3.1%)
↑20.0%prior 10
10
BUIC9 (2.3%)
→0.0%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · Vehicle unit records

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

Sex Distribution (319 persons with recorded sex)

Male197 (61.8%)
↑2.6%prior 192
Female122 (38.2%)
↓-10.3%prior 136

Source: Iowa Crash Data · ArcGIS Open Data · 2021-01-01 to 2021-12-31 · 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: 2021-01-01 through 2021-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2021-01-01 through 2021-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 228
  • Total persons involved: 467
  • Total vehicles involved: 384

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: 2021." Published September 9, 2026. Reporting period: 2021-01-01 to 2021-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2021-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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