Yearly Traffic Safety Analysis

290 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Bremer County, total vehicle crashes decreased by 29.1% from 409 in 2019 to 290 in 2020. This downward trend was accompanied by a 50% reduction in fatalities, which fell from 4 to 2 year-over-year. The most significant contributing factor in both periods was collisions with animals, though the count of these incidents also fell from 164 to 114.

290

▼ -29.1%was 409

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

81

▼ -1.2%was 82

Persons Injured

2

▼ -50.0%was 4

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Crash data for Bremer County shows a significant downward trend year-over-year. Total crashes fell from 409 in 2019 to 290 in 2020, a decrease of 29.1%. Fatalities were halved from 4 to 2, and total injuries remained nearly stable, decreasing slightly from 82 to 81.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 3↓-33.3%

81

Motorists Injured

Prior: 80↑1.3%

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 temporal patterns of crashes shifted between the two periods. In 2020, the peak day for crashes was Friday with 55 incidents, and the peak hour was 3 p.m. with 27 incidents. This contrasts with 2019, when the peak day was Monday (72 crashes) and the peak hour was 5 p.m. (42 crashes).

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 severity of crashes saw a notable shift year-over-year. The number of fatal crashes was cut in half, from 4 in 2019 to 2 in 2020, and the fatal crash rate per 100 crashes decreased from 0.98 to 0.69. While serious injury crashes also declined from 5 to 3, the count of minor injury crashes increased from 25 to 34, representing a larger share of total crashes (11.7% in 2020 vs. 6.1% in 2019).

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.7%
↓-50.0%prior 4
Serious Injury3serious injury crashes1%
↓-40.0%prior 5
Minor Injury34minor injury crashes11.7%
↑36.0%prior 25
Possible Injury28possible injury crashes9.7%
↓-20.0%prior 35
No Injury223no injury crashes76.9%
↓-34.4%prior 340

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

Collisions involving an animal remained the top contributing factor in both years, though the count of such incidents decreased by 30.5% from 164 in 2019 to 114 in 2020. The second most cited factor in 2020 was 'Lost Control' with 20 crashes, an increase from 17 crashes in the prior year. Conversely, crashes attributed to 'Followed too close' saw a 45.7% decrease in count, falling from 35 incidents in 2019 to 19 in 2020.

Officer-Reported Primary Contributing Cause

Animal114 (39.3%)↓-30.5%prior 164
Lost Control20 (6.9%)↑17.6%prior 17
Followed too close19 (6.6%)↓-45.7%prior 35
FTYROW: From stop sign18 (6.2%)↑5.9%prior 17
Ran off road - straight15 (5.2%)↓-37.5%prior 24
Driving too fast for conditions12 (4.1%)↓-42.9%prior 21
Ran off road - left11 (3.8%)↓-15.4%prior 13
FTYROW: Making left turn8 (2.8%)↓-20.0%prior 10
Driver Distraction: Other interior distraction7 (2.4%)↓-12.5%prior 8
Ran Traffic Signal6 (2.1%)

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

Road & Environmental Conditions

While the total number of crashes decreased across all conditions, the proportion of incidents on adverse road surfaces changed significantly. Crashes on snow-covered roads fell from 41 to 14, and those on icy roads dropped from 35 to 9. In contrast, crashes on dry roads decreased only slightly from 144 to 140, causing their share of total crashes to increase from 35.2% in 2019 to 48.3% in 2020. Proportions of crashes in different weather and lighting conditions remained relatively stable year-over-year.

Weather

Clear112 (58.3%)
↓-26.3%prior 152
Cloudy49 (25.5%)
↓-3.9%prior 51
Snow13 (6.8%)
↓-13.3%prior 15
Rain11 (5.7%)
↓-35.3%prior 17
Severe Winds2 (1.0%)
Other (explain in narrative)1 (0.5%)
Fog, smoke, smog1 (0.5%)
Freezing rain/drizzle1 (0.5%)
↓-88.9%prior 9
Blowing Snow1 (0.5%)
↓-92.9%prior 14
Sleet, hail1 (0.5%)

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

Lighting

Daylight131 (67.5%)
↓-28.0%prior 182
Dark - roadway not lighted29 (14.9%)
↓-42.0%prior 50
Dark - roadway lighted24 (12.4%)
↑26.3%prior 19
Dusk5 (2.6%)
Dawn4 (2.1%)
↓-20.0%prior 5
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry140 (72.2%)
↓-2.8%prior 144
Wet24 (12.4%)
↓-7.7%prior 26
Snow14 (7.2%)
↓-65.9%prior 41
Ice/frost9 (4.6%)
↓-74.3%prior 35
Gravel4 (2.1%)
↓-20.0%prior 5
Slush3 (1.5%)
↓-62.5%prior 8

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

Vehicles & Demographics

The top three vehicle makes involved in crashes—Chevrolet, Ford, and Dodge—were the same in both 2020 and 2019, with the count for each decreasing in line with the overall trend. The age distribution of persons involved in crashes also remained largely consistent between the two years. For instance, the 16-20 age group accounted for 14.8% of persons in 2020, compared to 14.5% in 2019, showing no significant shift in representation.

Top Vehicle Makes (412 vehicles)

1
FORD65 (15.8%)
↓-44.4%prior 117
2
CHEV54 (13.1%)
↓-53.0%prior 115
3
CHEVROLET31 (7.5%)
↑19.2%prior 26
4
DODG22 (5.3%)
↓-4.3%prior 23
5
TOYT20 (4.9%)
↓-25.9%prior 27
6
JEEP19 (4.6%)
↑11.8%prior 17
7
CHRY17 (4.1%)
↓-10.5%prior 19
8
BUIC12 (2.9%)
↓-20.0%prior 15
9
GMC11 (2.7%)
↓-42.1%prior 19
10
PONT11 (2.7%)
↓-38.9%prior 18

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

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

Sex Distribution (385 persons with recorded sex)

Male221 (57.4%)
↓-32.0%prior 325
Female164 (42.6%)
↓-26.5%prior 223

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: 290
  • Total persons involved: 613
  • Total vehicles involved: 412

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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