Yearly Traffic Safety Analysis

345 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Bremer County recorded 345 total crashes, a 9.7% decrease from the 382 crashes reported in 2016. The most significant year-over-year change was the reduction in traffic fatalities, which fell from one in 2016 to zero in 2017. Total injuries also saw a decline, dropping from 96 to 85 over the same period.

345

▼ -9.7%was 382

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

85

▼ -11.5%was 96

Persons Injured

0

▼ -100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Bremer County showed a downward trend from 2016 to 2017. The total number of crashes decreased by 9.7%, from 382 to 345. This decline was also reflected in the number of people injured, which fell by 11.5% from 96 to 85, and fatalities, which dropped from one to zero.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 1↓-100.0%

2

Pedestrians Injured

Prior: 1↑100.0%

83

Motorists Injured

Prior: 94↓-11.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted between the two periods. In 2017, the peak day for crashes was Thursday with 69 incidents, a change from Wednesday (62 incidents) in 2016. The peak hour for collisions also moved slightly earlier, from the 6 p.m. hour in 2016 (30 crashes) to the 5 p.m. hour in 2017 (34 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity improved with the elimination of fatal crashes, which fell from one incident in 2016 to zero in 2017. However, the proportion of crashes resulting in serious injuries increased from 1.8% (7 crashes) in 2016 to 2.9% (10 crashes) in 2017. The share of crashes with minor or possible injuries remained stable, at a combined 17.0% in 2016 and 16.8% in 2017.

Outcome by Severity (Crash Events)

Serious Injury10serious injury crashes2.9%
↑42.9%prior 7
Minor Injury29minor injury crashes8.4%
↓-12.1%prior 33
Possible Injury29possible injury crashes8.4%
↓-9.4%prior 32
No Injury277no injury crashes80.3%
↓-10.4%prior 309

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the leading contributing factor in both years, though the count of such incidents decreased by 15.2% from 138 in 2016 to 117 in 2017. 'Followed too close' was the second-most common factor in both periods, with its count falling from 35 to 30. Notably, crashes attributed to 'Lost Control' increased from 16 to 23, replacing 'Failure to yield from a stop sign' (which fell from 19 to 13 incidents) as the third-most cited factor in 2017.

Officer-Reported Primary Contributing Cause

Animal117 (33.9%)↓-15.2%prior 138
Followed too close30 (8.7%)↓-14.3%prior 35
Lost Control23 (6.7%)↑43.8%prior 16
FTYROW: Making left turn18 (5.2%)↑63.6%prior 11
Driving too fast for conditions15 (4.3%)↓-11.8%prior 17
Driver Distraction: Other interior distraction15 (4.3%)↑150.0%prior 6
Other (explain in narrative): Other13 (3.8%)↑44.4%prior 9
FTYROW: From stop sign13 (3.8%)↓-31.6%prior 19
Ran off road - straight13 (3.8%)↓-23.5%prior 17
Improper Backing9 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with the majority of incidents in both periods occurring in daylight and on dry roads. In 2017, there were 155 crashes on dry roads, down from 172 in 2016, which is in line with the overall decrease in crashes. However, there was a notable shift in specific adverse road conditions: crashes on gravel surfaces increased from 10 to 19, while incidents on icy or frosty roads decreased from 21 to 10.

Weather

Clear151 (64.3%)
↓-3.8%prior 157
Cloudy41 (17.4%)
↓-16.3%prior 49
Snow17 (7.2%)
↑30.8%prior 13
Rain15 (6.4%)
→0.0%prior 15
Freezing rain/drizzle6 (2.6%)
Severe Winds2 (0.9%)
Blowing Snow2 (0.9%)
Fog, smoke, smog1 (0.4%)
↓-90.0%prior 10

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight170 (71.7%)
↓-9.6%prior 188
Dark - roadway not lighted30 (12.7%)
↓-28.6%prior 42
Dark - roadway lighted27 (11.4%)
↑12.5%prior 24
Dusk5 (2.1%)
Dawn4 (1.7%)
Dark - unknown roadway lighting1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry155 (65.7%)
↓-9.9%prior 172
Wet31 (13.1%)
↓-11.4%prior 35
Snow21 (8.9%)
↑31.3%prior 16
Gravel19 (8.1%)
↑90.0%prior 10
Ice/frost10 (4.2%)
↓-52.4%prior 21

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Toyota being the most frequent in both 2016 and 2017. Combining abbreviated and full names, the number of Chevrolet vehicles involved decreased from 150 to 107, while Ford vehicles increased from 87 to 97. An analysis of persons involved in crashes shows a notable decrease in the 65+ age group, which fell from 86 individuals in 2016 to 51 in 2017. The representation of other age groups remained relatively proportional to the overall decrease in persons involved.

Top Vehicle Makes (515 vehicles)

1
FORD97 (18.8%)
↑11.5%prior 87
2
CHEV70 (13.6%)
→0.0%prior 70
3
CHEVROLET37 (7.2%)
↓-53.8%prior 80
4
TOYT25 (4.9%)
↑4.2%prior 24
5
TOYOTA21 (4.1%)
↓-12.5%prior 24
6
GMC20 (3.9%)
↑17.6%prior 17
7
JEEP17 (3.3%)
↑13.3%prior 15
8
DODG17 (3.3%)
→0.0%prior 17
9
PONT17 (3.3%)
↑88.9%prior 9
10
DODGE14 (2.7%)
↓-33.3%prior 21

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (382 persons with recorded sex)

Male197 (51.6%)
↓-13.6%prior 228
Female185 (48.4%)
↓-19.9%prior 231

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 345
  • Total persons involved: 586
  • Total vehicles involved: 515

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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