Yearly Traffic Safety Analysis

382 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Bremer County recorded 382 total crashes, a 9.7% decrease from the 423 crashes in 2015. While overall crashes and injuries declined, the number of crashes involving a DUI driver increased from 4 to 10 year-over-year.

382

▼ -9.7%was 423

Total Crash Events

1

Persons Killed

96

▼ -13.5%was 111

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Bremer County showed a downward trend in 2016 compared to the prior year. Total crashes decreased by 9.7% from 423 to 382, and the number of people injured fell by 13.5% from 111 to 96. The number of fatalities remained unchanged, with one person killed in both 2015 and 2016.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 3↓-66.7%

1

Cyclists Injured

Prior: 3↓-66.7%

94

Motorists Injured

Prior: 105↓-10.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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 year-over-year. In 2016, the peak day for crashes was Wednesday with 62 incidents, changing from Friday (78 incidents) in 2015. Similarly, the peak hour for crashes moved to 6 p.m. (30 crashes) in 2016 from 3 p.m. (38 crashes) in the previous year.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of fatal crashes remained stable at one for both years, the fatal crash rate per 100 crashes increased slightly from 0.24 to 0.26. The proportion of crashes resulting in serious injuries grew from 1.2% (5 crashes) in 2015 to 1.8% (7 crashes) in 2016. Conversely, the share of possible injury crashes decreased from 11.8% of all crashes in 2015 to 8.4% in 2016.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
→0.0%prior 1
Serious Injury7serious injury crashes1.8%
↑40.0%prior 5
Minor Injury33minor injury crashes8.6%
↑3.1%prior 32
Possible Injury32possible injury crashes8.4%
↓-36.0%prior 50
No Injury309no injury crashes80.9%
↓-7.8%prior 335

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the leading contributing factor in both periods, though the count of such incidents decreased from 167 in 2015 to 138 in 2016. The number of crashes attributed to 'Followed too close' increased by 66.7%, from 21 to 35, making it the second most common factor in 2016. Meanwhile, crashes due to 'Lost Control' dropped from 28 to 16, and 'FTYROW: Making left turn' incidents fell from 23 to 11.

Officer-Reported Primary Contributing Cause

Animal138 (36.1%)↓-17.4%prior 167
Followed too close35 (9.2%)↑66.7%prior 21
FTYROW: From stop sign19 (5%)↑35.7%prior 14
Ran off road - straight17 (4.5%)↑30.8%prior 13
Driving too fast for conditions17 (4.5%)↓-22.7%prior 22
Lost Control16 (4.2%)↓-42.9%prior 28
FTYROW: Making left turn11 (2.9%)↓-52.2%prior 23
Ran off road - left10 (2.6%)↓-9.1%prior 11
Other (explain in narrative): Other9 (2.4%)↓-30.8%prior 13
Ran Stop Sign8 (2.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions were largely consistent year-over-year, with most incidents occurring in daylight and on dry roads in both 2016 and 2015. There was a notable decrease in crashes on snow-covered roads, which fell from 36 incidents in 2015 to 16 in 2016. Crashes on roads with ice or frost saw a slight increase from 18 to 21 incidents.

Weather

Clear157 (61.1%)
↑1.3%prior 155
Cloudy49 (19.1%)
↓-25.8%prior 66
Rain15 (5.8%)
↓-16.7%prior 18
Snow13 (5.1%)
↓-45.8%prior 24
Fog, smoke, smog10 (3.9%)
Severe Winds4 (1.6%)
Blowing Snow4 (1.6%)
Freezing rain/drizzle3 (1.2%)
Sleet, hail2 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight188 (72.3%)
↓-1.1%prior 190
Dark - roadway not lighted42 (16.2%)
↓-10.6%prior 47
Dark - roadway lighted24 (9.2%)
↑9.1%prior 22
Dawn4 (1.5%)
Dusk1 (0.4%)
↓-87.5%prior 8
Dark - unknown roadway lighting1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry172 (66.7%)
↑10.3%prior 156
Wet35 (13.6%)
↑12.9%prior 31
Ice/frost21 (8.1%)
↑16.7%prior 18
Snow16 (6.2%)
↓-55.6%prior 36
Gravel10 (3.9%)
↓-50.0%prior 20
Slush3 (1.2%)
Mud, dirt1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The number of individuals aged 16-20 involved in crashes decreased from 142 in 2015 to 100 in 2016. In contrast, involvement for the 35-44 age group increased from 88 to 100 persons. Combining abbreviated and full names, Chevrolet and Ford were the top two makes involved in crashes in both years; Chevrolet-involved vehicles increased from 127 to 150, while Ford-involved vehicles decreased from 102 to 87.

Top Vehicle Makes (566 vehicles)

1
FORD87 (15.4%)
↓-14.7%prior 102
2
CHEVROLET80 (14.1%)
↑40.4%prior 57
3
CHEV70 (12.4%)
→0.0%prior 70
4
TOYT24 (4.2%)
↑9.1%prior 22
5
TOYOTA24 (4.2%)
↑33.3%prior 18
6
DODGE21 (3.7%)
↑23.5%prior 17
7
GMC17 (3%)
↑30.8%prior 13
8
DODG17 (3%)
↓-19.0%prior 21
9
CHRYSLER16 (2.8%)
↓-5.9%prior 17
10
JEEP15 (2.7%)
↑15.4%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (459 persons with recorded sex)

Female231 (50.3%)
↓-4.9%prior 243
Male228 (49.7%)
↓-26.2%prior 309

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 382
  • Total persons involved: 633
  • Total vehicles involved: 566

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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