Yearly Traffic Safety Analysis

324 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In 2021, Benton County recorded 324 total crashes, a 24.1% increase from the 261 crashes reported in 2020. The most significant year-over-year change was in crash outcomes, with total fatalities tripling from 3 in 2020 to 9 in 2021. Total injuries also rose from 120 to 151 during the same period.

324

▲ 24.1%was 261

Total Crash Events

9

▲ 200.0%was 3

Persons Killed

151

▲ 25.8%was 120

Persons Injured

9

▲ 200.0%was 3

Fatal Crash Events

Note: "Persons Killed" (9) counts individual fatalities across all crash events. "Fatal" in the severity table below (9) 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 Benton County showed a notable increase from 2020 to 2021. Total crashes rose by 24.1%, from 261 to 324. This upward trend was also reflected in crash severity, with total injuries increasing by 25.8% and fatalities tripling from 3 to 9.

Vulnerable Road User Casualties

1

Cyclists Killed

Prior: 0→%

8

Motorists Killed

Prior: 3↑166.7%

0

Cyclists Injured

Prior: 1↓-100.0%

151

Motorists Injured

Prior: 119↑26.9%

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 shifted between 2020 and 2021. The peak day for collisions moved from Monday (54 crashes) in 2020 to Friday (56 crashes) in 2021. Similarly, the peak hour for crashes changed from the 7 a.m. morning commute hour in 2020 (20 crashes) to the 3 p.m. afternoon hour in 2021 (27 crashes).

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

Crash severity worsened significantly in 2021 compared to the prior year. The number of fatal crashes tripled from 3 to 9, and the fatal crash rate per 100 crashes increased from 1.15 to 2.78. Crashes resulting in serious injuries more than doubled, rising from 7 in 2020 to 18 in 2021, representing 5.6% of all crashes compared to 2.7% the previous year. The proportion of no-injury crashes remained stable at approximately 63% in both periods.

Outcome by Severity (Crash Events)

Fatal9fatal crashes2.8%
↑200.0%prior 3
Serious Injury18serious injury crashes5.6%
↑157.1%prior 7
Minor Injury50minor injury crashes15.4%
↓-2.0%prior 51
Possible Injury41possible injury crashes12.7%
↑17.1%prior 35
No Injury206no injury crashes63.6%
↑24.8%prior 165

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 leading contributing factors for crashes showed some shifts between 2020 and 2021. While collisions involving an 'Animal' remained a primary factor, their count slightly decreased from 71 to 68. Conversely, crashes attributed to 'Lost Control' increased by 53%, from 32 to 49 incidents. 'Failure to yield right of way from a stop sign' saw a notable rise, with associated crashes increasing from 8 in 2020 to 21 in 2021.

Officer-Reported Primary Contributing Cause

Animal68 (21%)↓-4.2%prior 71
Lost Control49 (15.1%)↑53.1%prior 32
Driving too fast for conditions26 (8%)↑23.8%prior 21
Ran off road - straight23 (7.1%)↑76.9%prior 13
FTYROW: From stop sign21 (6.5%)↑162.5%prior 8
Followed too close14 (4.3%)↑27.3%prior 11
Ran off road - left11 (3.4%)↑22.2%prior 9
Operating vehicle in an reckless, erratic, careless, negligent manner9 (2.8%)↑50.0%prior 6
Driver Distraction: Other interior distraction9 (2.8%)↑28.6%prior 7
Driver Distraction: Inattentive/lost in thought9 (2.8%)↑12.5%prior 8

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

Road & Environmental Conditions

The conditions under which crashes occurred showed some year-over-year changes. The proportion of collisions on dry road surfaces increased from 49.4% in 2020 to 56.5% in 2021. Notably, crashes on snow-covered roads more than tripled, rising from 8 incidents in 2020 to 28 in 2021. While the majority of crashes in both years occurred during daylight hours and in clear weather, the absolute number of incidents under these conditions grew in line with the overall increase in crashes.

Weather

Clear179 (65.6%)
↑28.8%prior 139
Cloudy45 (16.5%)
↑66.7%prior 27
Rain13 (4.8%)
↑30.0%prior 10
Snow10 (3.7%)
→0.0%prior 10
Blowing Snow10 (3.7%)
Freezing rain/drizzle7 (2.6%)
Fog, smoke, smog6 (2.2%)
Severe Winds2 (0.7%)
Other (explain in narrative)1 (0.4%)

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

Lighting

Daylight158 (58.3%)
↑26.4%prior 125
Dark - roadway not lighted72 (26.6%)
↑35.8%prior 53
Dark - roadway lighted21 (7.7%)
↑90.9%prior 11
Dusk11 (4.1%)
Dawn8 (3.0%)
↓-27.3%prior 11
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry183 (67.0%)
↑41.9%prior 129
Snow28 (10.3%)
↑250.0%prior 8
Wet22 (8.1%)
↓-15.4%prior 26
Gravel17 (6.2%)
↓-15.0%prior 20
Ice/frost15 (5.5%)
↑7.1%prior 14
Slush7 (2.6%)
Other (explain in narrative)1 (0.4%)

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

Vehicles & Demographics

Analysis of vehicles and persons involved in crashes reveals shifts in demographics. The top vehicle makes involved in collisions remained consistent, with Chevrolet (99 vehicles, combining 'Chev' and 'Chevrolet' entries) and Ford (85 vehicles) leading in 2021, up from 75 and 67 respectively in 2020. The age distribution of individuals involved in crashes changed; the 26-34 age group was the most represented in 2020 (108 persons), whereas the 16-20 age group was the largest cohort in 2021 (96 persons). Notably, the number of persons aged 65 and older involved in crashes increased by 52%, from 48 in 2020 to 73 in 2021.

Top Vehicle Makes (456 vehicles)

1
FORD85 (18.6%)
↑26.9%prior 67
2
CHEV68 (14.9%)
↑28.3%prior 53
3
CHEVROLET31 (6.8%)
↑40.9%prior 22
4
TOYT21 (4.6%)
↑16.7%prior 18
5
DODG21 (4.6%)
→0.0%prior 21
6
GMC18 (3.9%)
↑50.0%prior 12
7
BUIC14 (3.1%)
↑40.0%prior 10
8
JEEP13 (2.9%)
↓-13.3%prior 15
9
KIA12 (2.6%)
↑9.1%prior 11
10
FREIGHTLINER11 (2.4%)
↑83.3%prior 6

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

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

Sex Distribution (350 persons with recorded sex)

Male237 (67.7%)
↑9.7%prior 216
Female113 (32.3%)
↓-9.6%prior 125

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: 324
  • Total persons involved: 594
  • Total vehicles involved: 456

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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