Yearly Traffic Safety Analysis

3,334 CRASHES IN
IOWA, IA
2021

All metrics benchmarked against2020

In 2021, Linn County recorded 3,334 total crashes, a 14.3% increase from the 2,916 crashes reported in 2020. This year-over-year rise in collisions was accompanied by an 11.6% increase in total injuries, which grew from 929 to 1,037. Fatalities also increased slightly from 12 in the prior period to 13 in the current period.

3,334

14.3%was 2,916

Total Crash Events

13

8.3%was 12

Persons Killed

1,037

11.6%was 929

Persons Injured

12

9.1%was 11

Fatal Crash Events

Note: "Persons Killed" (13) counts individual fatalities across all crash events. "Fatal" in the severity table below (12) 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

Overall crash trends in Linn County show a notable increase year-over-year. Total crashes rose by 14.3%, from 2,916 in 2020 to 3,334 in 2021. Correspondingly, total injuries increased by 11.6% to 1,037, while fatalities saw a slight rise from 12 to 13.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2-50.0%

0

Cyclists Killed

Prior: 00.0%

12

Motorists Killed

Prior: 1020.0%

0

Other Killed

Prior: 00.0%

29

Pedestrians Injured

Prior: 2611.5%

14

Cyclists Injured

Prior: 15-6.7%

989

Motorists Injured

Prior: 88212.1%

5

Other Injured

Prior: 6-16.7%

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

Temporal analysis shows that while Friday remained the peak day for crashes in both 2020 and 2021, the number of Friday crashes increased from 489 to 602. The peak hour for collisions shifted slightly, moving from 5 p.m. in 2020 (245 crashes) to 4 p.m. in 2021 (292 crashes). Overall, crash volumes increased during the afternoon commute hours year-over-year.

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

The number of fatal crashes increased from 11 in 2020 to 12 in 2021, though the overall fatal crash rate per 100 crashes decreased slightly from 0.38 to 0.36. The proportion of crashes resulting in minor injuries increased from an 8.6% share to a 9.4% share of all incidents. Crashes involving serious injuries increased in count from 51 to 55, but their share of total crashes saw a minor dip from 1.7% to 1.6%.

Severity is per crash event (most severe injury). 12 fatal crash events resulted in 13 persons killed.

Outcome by Severity (Crash Events)

Fatal12fatal crashes0.4%
9.1%prior 11
Serious Injury55serious injury crashes1.6%
7.8%prior 51
Minor Injury312minor injury crashes9.4%
23.8%prior 252
Possible Injury512possible injury crashes15.4%
8.9%prior 470
No Injury2,443no injury crashes73.3%
14.6%prior 2,132

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 remained consistent between 2020 and 2021. 'Followed too close' was the top factor in both years, with its count increasing from 390 to 429. Crashes involving an 'Animal' rose from 227 to 282, becoming the second most common factor in 2021, up from third in 2020. 'Ran off road - left' also saw an increase in incidents, from 251 to 281. Notably, crashes attributed to 'Driving too fast for conditions' grew in count by 36.5%, from 115 to 157.

Officer-Reported Primary Contributing Cause

Followed too close429 (12.9%)10.0%prior 390
Animal282 (8.5%)24.2%prior 227
Ran off road - left281 (8.4%)12.0%prior 251
FTYROW: Making left turn214 (6.4%)13.8%prior 188
FTYROW: From stop sign210 (6.3%)14.1%prior 184
Other (explain in narrative): Other202 (6.1%)21.0%prior 167
Ran Traffic Signal162 (4.9%)13.3%prior 143
Driving too fast for conditions157 (4.7%)36.5%prior 115
Lost Control111 (3.3%)0.9%prior 110
Ran Stop Sign103 (3.1%)22.6%prior 84

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 distribution of crashes across different environmental conditions remained largely consistent year-over-year. The majority of crashes in both 2021 (70.3%) and 2020 (71.3%) occurred on dry road surfaces. Similarly, most incidents happened in clear weather (61.5% in 2021 vs. 61.0% in 2020) and during daylight hours (68.5% in 2021 vs. 66.8% in 2020). There was a modest increase in the share of crashes occurring on icy or frosty roads, which rose from 3.5% of crashes in 2020 to 4.9% in 2021.

Weather

Clear2,051 (66.3%)
15.4%prior 1,777
Cloudy687 (22.2%)
16.0%prior 592
Rain186 (6.0%)
6.9%prior 174
Snow76 (2.5%)
-34.5%prior 116
Freezing rain/drizzle45 (1.5%)
21.6%prior 37
Blowing Snow20 (0.6%)
150.0%prior 8
Fog, smoke, smog10 (0.3%)
-9.1%prior 11
Other (explain in narrative)8 (0.3%)
Severe Winds6 (0.2%)
-33.3%prior 9
Sleet, hail5 (0.2%)

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

Lighting

Daylight2,285 (73.8%)
17.3%prior 1,948
Dark - roadway lighted501 (16.2%)
9.4%prior 458
Dark - roadway not lighted173 (5.6%)
-4.9%prior 182
Dusk86 (2.8%)
-1.1%prior 87
Dawn44 (1.4%)
-13.7%prior 51
Dark - unknown roadway lighting8 (0.3%)
14.3%prior 7

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

Road Surface

Dry2,343 (75.7%)
12.8%prior 2,078
Wet393 (12.7%)
-3.7%prior 408
Ice/frost165 (5.3%)
61.8%prior 102
Snow131 (4.2%)
45.6%prior 90
Slush31 (1.0%)
0.0%prior 31
Gravel17 (0.5%)
41.7%prior 12
Sand8 (0.3%)
Other (explain in narrative)3 (0.1%)
-57.1%prior 7
Mud, dirt2 (0.1%)
Oil1 (0.0%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes showed a consistent pattern, with Chevrolet, Ford, and Toyota being the most common in both periods, and all saw an increase in crash involvement counts. An analysis of persons involved in crashes shows a notable increase in the 16-20 age group, whose count rose from 884 in 2020 to 1,038 in 2021. This increased their share of total persons involved from 12.7% to 14.1%.

Top Vehicle Makes (6,121 vehicles)

1
FORD1,023 (16.7%)
12.2%prior 912
2
CHEV695 (11.4%)
3.7%prior 670
3
CHEVROLET383 (6.3%)
19.3%prior 321
4
TOYT381 (6.2%)
11.1%prior 343
5
TOYOTA244 (4%)
40.2%prior 174
6
HOND236 (3.9%)
29.0%prior 183
7
JEEP227 (3.7%)
21.4%prior 187
8
KIA211 (3.4%)
42.6%prior 148
9
NISS183 (3%)
10.2%prior 166
10
DODG181 (3%)
11.0%prior 163

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

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

Sex Distribution (5,219 persons with recorded sex)

Male2,865 (54.9%)
-0.1%prior 2,869
Female2,354 (45.1%)
9.6%prior 2,148

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: 3,334
  • Total persons involved: 7,337
  • Total vehicles involved: 6,121

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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