Yearly Traffic Safety Analysis

370 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Bremer County recorded 370 total crashes, a 5.4% increase from the 351 crashes documented in 2021. While total collisions rose, the number of fatalities decreased from 6 to 3. A notable year-over-year change was the number of crashes involving a driver under the influence (DUI), which increased from 11 in 2021 to 20 in 2022.

370

▲ 5.4%was 351

Total Crash Events

3

▼ -50.0%was 6

Persons Killed

72

▼ -7.7%was 78

Persons Injured

3

▼ -50.0%was 6

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Bremer County showed an upward trend in 2022, increasing by 5.4% from 351 to 370 incidents. Despite the rise in total collisions, the outcomes were less severe on average. Total fatalities were halved from 6 to 3, and the number of people injured decreased by 7.7% from 78 to 72.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 6↓-50.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

1

Cyclists Injured

Prior: 2↓-50.0%

70

Motorists Injured

Prior: 74↓-5.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some shifts between 2021 and 2022. The peak day for crashes moved from Friday (69 crashes) in the prior period to Thursday (65 crashes) in the current period. However, the peak hour for collisions remained consistent at the 6 p.m. hour, which saw 30 crashes in 2022 compared to 28 in 2021.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity generally decreased in 2022 compared to the previous year. The number of fatal crashes fell from 6 to 3, reducing the fatal crash rate from 1.71 to 0.81 per 100 crashes. The proportion of serious injury crashes also declined from 2.6% to 1.4% of all incidents, while crashes resulting in no injuries increased their share from 81.8% in 2021 to 85.1% in 2022.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.8%
↓-50.0%prior 6
Serious Injury5serious injury crashes1.4%
↓-44.4%prior 9
Minor Injury30minor injury crashes8.1%
↑30.4%prior 23
Possible Injury17possible injury crashes4.6%
↓-34.6%prior 26
No Injury315no injury crashes85.1%
↑9.8%prior 287

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both periods, with the count of such incidents increasing by 9.8% from 143 in 2021 to 157 in 2022. 'Driving too fast for conditions' saw a significant rise, with the count of related crashes doubling from 13 to 26, making it the second-most cited factor in 2022. In contrast, crashes attributed to 'Followed too close' decreased in count from 28 to 23.

Officer-Reported Primary Contributing Cause

Animal157 (42.4%)↑9.8%prior 143
Driving too fast for conditions26 (7%)↑100.0%prior 13
Followed too close23 (6.2%)↓-17.9%prior 28
Lost Control17 (4.6%)↑54.5%prior 11
Ran off road - left12 (3.2%)↓-7.7%prior 13
Ran off road - straight11 (3%)↓-42.1%prior 19
Ran Stop Sign11 (3%)↓-15.4%prior 13
FTYROW: Making left turn11 (3%)↑10.0%prior 10
FTYROW: From stop sign11 (3%)↓-35.3%prior 17
Other (explain in narrative): Other9 (2.4%)↓-55.0%prior 20

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While clear weather and daylight hours were the most common conditions in both periods, there was a shift in road surface conditions. Crashes on dry roads decreased from 175 to 145 year-over-year. Conversely, incidents on adverse surfaces increased, with crashes on roads with ice or frost more than doubling from 11 to 24, and crashes on snow-covered roads rising from 20 to 26.

Weather

Clear144 (64.6%)
↓-11.7%prior 163
Cloudy35 (15.7%)
↓-10.3%prior 39
Snow21 (9.4%)
↑90.9%prior 11
Rain11 (4.9%)
↑83.3%prior 6
Blowing Snow6 (2.7%)
Freezing rain/drizzle3 (1.3%)
Fog, smoke, smog1 (0.4%)
Other (explain in narrative)1 (0.4%)
Severe Winds1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight151 (68.0%)
↓-6.8%prior 162
Dark - roadway not lighted32 (14.4%)
↓-11.1%prior 36
Dark - roadway lighted29 (13.1%)
↑81.3%prior 16
Dusk7 (3.2%)
↓-12.5%prior 8
Dawn2 (0.9%)
↓-60.0%prior 5
Dark - unknown roadway lighting1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry145 (65.0%)
↓-17.1%prior 175
Snow26 (11.7%)
↑30.0%prior 20
Ice/frost24 (10.8%)
↑118.2%prior 11
Wet21 (9.4%)
↑50.0%prior 14
Gravel4 (1.8%)
↓-33.3%prior 6
Slush2 (0.9%)
Other (explain in narrative)1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet leading in both years; the count of Ford vehicles in crashes increased from 89 to 111, and Chevrolet vehicles from 82 to 93. An analysis of persons involved shows a shift in age demographics, with increased involvement from the 26-34 age group (from 85 to 113 persons) and the 35-44 age group (from 93 to 122 persons). The 16-20 age group saw a slight decrease in involvement, from 104 to 97 persons.

Top Vehicle Makes (521 vehicles)

1
FORD111 (21.3%)
↑24.7%prior 89
2
CHEV93 (17.9%)
↑13.4%prior 82
3
DODG32 (6.1%)
↑77.8%prior 18
4
TOYT27 (5.2%)
↑12.5%prior 24
5
CHEVROLET25 (4.8%)
↑8.7%prior 23
6
GMC20 (3.8%)
↓-35.5%prior 31
7
NISS18 (3.5%)
↑50.0%prior 12
8
JEEP17 (3.3%)
→0.0%prior 17
9
HOND14 (2.7%)
→0.0%prior 14
10
CHRY14 (2.7%)
↑16.7%prior 12

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

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

Sex Distribution (488 persons with recorded sex)

Male270 (55.3%)
↑23.3%prior 219
Female218 (44.7%)
↑19.1%prior 183

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 370
  • Total persons involved: 768
  • Total vehicles involved: 521

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: 2022." Published September 9, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2022-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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