Yearly Traffic Safety Analysis

2,916 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Linn County recorded 2,916 total crashes, a 21.1% decrease from the 3,697 crashes reported in 2019. This downturn was also reflected in crash outcomes, with total injuries falling by 24.1% from 1,224 to 929 and fatalities decreasing from 15 to 12. The most significant year-over-year shift was this overall reduction in traffic collisions and resulting casualties.

2,916

▼ -21.1%was 3,697

Total Crash Events

12

▼ -20.0%was 15

Persons Killed

929

▼ -24.1%was 1,224

Persons Injured

11

▼ -21.4%was 14

Fatal Crash Events

Note: "Persons Killed" (12) counts individual fatalities across all crash events. "Fatal" in the severity table below (11) 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

Traffic safety trends in Linn County showed a marked improvement from 2019 to 2020. Total crashes fell by 781 incidents, a 21.1% year-over-year reduction. This positive trend extended to crash severity, with 295 fewer injuries and 3 fewer fatalities recorded in 2020.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 1↑100.0%

0

Cyclists Killed

Prior: 0→0.0%

10

Motorists Killed

Prior: 14↓-28.6%

0

Other Killed

Prior: 0→0.0%

26

Pedestrians Injured

Prior: 21↑23.8%

15

Cyclists Injured

Prior: 30↓-50.0%

882

Motorists Injured

Prior: 1,169↓-24.6%

6

Other Injured

Prior: 4↑50.0%

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

The timing of crashes shifted slightly between the two periods, with the peak day for collisions moving from Thursday (618 crashes) in 2019 to Friday (489 crashes) in 2020. Similarly, the peak hour for crashes moved from 4 p.m. in the prior year to 5 p.m. in the current year. Overall crash volumes during weekday afternoon commute hours were substantially lower in 2020 compared to 2019.

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

The overall severity distribution of crashes remained consistent year-over-year, despite a large drop in total incidents. The proportion of crashes resulting in a fatality was stable at 0.4% in both 2019 and 2020. Crashes involving a serious injury saw a slight proportional increase, accounting for 1.7% of all collisions in 2020 compared to 1.5% in the previous year.

Severity is per crash event (most severe injury). 11 fatal crash events resulted in 12 persons killed.

Outcome by Severity (Crash Events)

Fatal11fatal crashes0.4%
↓-21.4%prior 14
Serious Injury51serious injury crashes1.7%
↓-8.9%prior 56
Minor Injury252minor injury crashes8.6%
↓-27.6%prior 348
Possible Injury470possible injury crashes16.1%
↓-15.9%prior 559
No Injury2,132no injury crashes73.1%
↓-21.6%prior 2,720

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

The primary contributing factors to crashes were unchanged, with 'Followed too close' remaining the top-cited reason in both years, though its count decreased from 442 to 390. The second and third most common factors, 'Ran off road - left' and 'Animal', also retained their rankings while seeing their incident counts fall by 24.2% and 23.3%, respectively. Crashes attributed to 'Driving too fast for conditions' saw a notable 39.5% reduction in count, from 190 incidents in 2019 to 115 in 2020.

Officer-Reported Primary Contributing Cause

Followed too close390 (13.4%)↓-11.8%prior 442
Ran off road - left251 (8.6%)↓-24.2%prior 331
Animal227 (7.8%)↓-23.3%prior 296
FTYROW: Making left turn188 (6.4%)↓-27.7%prior 260
FTYROW: From stop sign184 (6.3%)↓-17.9%prior 224
Other (explain in narrative): Other167 (5.7%)↓-28.6%prior 234
Ran Traffic Signal143 (4.9%)↓-25.9%prior 193
Driving too fast for conditions115 (3.9%)↓-39.5%prior 190
Lost Control110 (3.8%)↓-26.7%prior 150
Driver Distraction: Other interior distraction89 (3.1%)↑3.5%prior 86

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

Road & Environmental Conditions

The proportion of crashes occurring in clear weather and on dry road surfaces increased in 2020 compared to the prior year. Crashes on dry roads made up 71.3% of the total in 2020, up from 63.4% in 2019. Correspondingly, the share of collisions on adverse surfaces like wet, icy, or snowy roads decreased from 28.6% in 2019 to 21.6% in 2020. The percentage of crashes happening in daylight conditions remained stable at approximately 67% for both periods.

Weather

Clear1,777 (65.0%)
↓-10.4%prior 1,983
Cloudy592 (21.7%)
↓-33.2%prior 886
Rain174 (6.4%)
↓-23.3%prior 227
Snow116 (4.2%)
↓-37.0%prior 184
Freezing rain/drizzle37 (1.4%)
↓-42.2%prior 64
Fog, smoke, smog11 (0.4%)
↓-50.0%prior 22
Severe Winds9 (0.3%)
↑28.6%prior 7
Blowing Snow8 (0.3%)
↓-80.5%prior 41
Other (explain in narrative)4 (0.1%)
↓-50.0%prior 8
Sleet, hail4 (0.1%)
↓-55.6%prior 9

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

Lighting

Daylight1,948 (71.3%)
↓-21.5%prior 2,481
Dark - roadway lighted458 (16.8%)
↓-22.4%prior 590
Dark - roadway not lighted182 (6.7%)
↓-3.2%prior 188
Dusk87 (3.2%)
↓-18.7%prior 107
Dawn51 (1.9%)
↓-15.0%prior 60
Dark - unknown roadway lighting7 (0.3%)
↓-30.0%prior 10

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

Road Surface

Dry2,078 (76.0%)
↓-11.3%prior 2,344
Wet408 (14.9%)
↓-26.5%prior 555
Ice/frost102 (3.7%)
↓-46.3%prior 190
Snow90 (3.3%)
↓-65.5%prior 261
Slush31 (1.1%)
↓-38.0%prior 50
Gravel12 (0.4%)
↓-20.0%prior 15
Other (explain in narrative)7 (0.3%)
↑40.0%prior 5
Mud, dirt3 (0.1%)
↓-57.1%prior 7
Sand2 (0.1%)
Water (standing or moving)1 (0.0%)

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

Vehicles & Demographics

The makes of vehicles most frequently involved in crashes remained consistent, with Chevrolet and Ford leading in both 2019 and 2020, though the number of vehicles involved decreased in line with the overall trend. The age distribution of persons involved in crashes also showed little change year-over-year. For example, the 26-34 age group represented a stable 16.3% share of all individuals in both years.

Top Vehicle Makes (5,368 vehicles)

1
FORD912 (17%)
↓-22.4%prior 1,176
2
CHEV670 (12.5%)
↓-21.0%prior 848
3
TOYT343 (6.4%)
↓-25.8%prior 462
4
CHEVROLET321 (6%)
↓-20.0%prior 401
5
JEEP187 (3.5%)
↓-7.4%prior 202
6
HOND183 (3.4%)
↓-25.0%prior 244
7
TOYOTA174 (3.2%)
↓-18.7%prior 214
8
NISS166 (3.1%)
↓-18.6%prior 204
9
DODG163 (3%)
↓-33.2%prior 244
10
GMC158 (2.9%)
↓-6.5%prior 169

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

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

Sex Distribution (5,017 persons with recorded sex)

Male2,869 (57.2%)
↓-17.4%prior 3,474
Female2,148 (42.8%)
↓-29.3%prior 3,037

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: 2,916
  • Total persons involved: 6,950
  • Total vehicles involved: 5,368

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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