Yearly Traffic Safety Analysis

357 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Bremer County recorded 357 total crashes, representing a 3.5% increase from the 345 crashes reported in 2017. The most significant year-over-year change was the emergence of fatal crashes, with three incidents resulting in three fatalities in 2018 compared to none in the prior year. While the total number of injuries remained stable at 85 for both periods, the number of serious injury crashes decreased by half.

357

▲ 3.5%was 345

Total Crash Events

3

Persons Killed

85

Persons Injured

3

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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Bremer County showed a slight increase in 2018, with total incidents rising to 357 from 345 in the previous year. While the overall number of injuries was unchanged at 85 persons, the county experienced three fatal crashes in 2018 after having none in 2017. This marks a negative turn in crash severity despite a relatively stable total crash volume.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 0→%

85

Motorists Injured

Prior: 83↑2.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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 years. In 2018, Monday was the peak day for crashes with 67 incidents, a change from Thursday (69 incidents) in 2017. The peak hour for collisions remained 5 p.m. in both periods, although the crash count during this hour fell from 34 to 29. Crash volumes in both years were highest in the later months, with November being a top month for incidents in both 2018 (46 crashes) and 2017 (49 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened in 2018, with three fatal crashes recorded compared to zero in 2017, causing the fatal crash rate to rise from 0% to 0.8%. In contrast, the number of serious injury crashes was halved, decreasing from 10 in 2017 to 5 in 2018. The proportion of crashes resulting in no injuries remained consistent, accounting for 80.7% of incidents in 2018 versus 80.3% in 2017.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.8%
Serious Injury5serious injury crashes1.4%
↓-50.0%prior 10
Minor Injury27minor injury crashes7.6%
↓-6.9%prior 29
Possible Injury34possible injury crashes9.5%
↑17.2%prior 29
No Injury288no injury crashes80.7%
↑4.0%prior 277

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals were the leading contributing factor in both years, with the count increasing from 117 in 2017 to 129 in 2018. 'Followed too close' remained the second-most common factor but saw its count decrease from 30 to 26 incidents. 'Failure to yield right of way from a stop sign' became more prevalent, increasing from 13 crashes in 2017 to 18 in 2018. Conversely, crashes attributed to driving under the influence (DUI) fell from 10 in 2017 to 6 in 2018.

Officer-Reported Primary Contributing Cause

Animal129 (36.1%)↑10.3%prior 117
Followed too close26 (7.3%)↓-13.3%prior 30
Lost Control25 (7%)↑8.7%prior 23
FTYROW: From stop sign18 (5%)↑38.5%prior 13
Driving too fast for conditions16 (4.5%)↑6.7%prior 15
Ran off road - left15 (4.2%)↑114.3%prior 7
Other (explain in narrative): Other14 (3.9%)↑7.7%prior 13
Ran off road - straight14 (3.9%)↑7.7%prior 13
FTYROW: Making left turn12 (3.4%)↓-33.3%prior 18
Ran Stop Sign9 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather on dry roads. There was a notable increase in crashes on roads with ice or frost, which rose from 10 incidents in 2017 to 18 in 2018. While daylight crashes remained most common, collisions on unlighted dark roadways increased from 30 to 43, whereas crashes on lighted dark roadways fell from 27 to 14.

Weather

Clear133 (55.4%)
↓-11.9%prior 151
Cloudy63 (26.3%)
↑53.7%prior 41
Snow17 (7.1%)
→0.0%prior 17
Rain10 (4.2%)
↓-33.3%prior 15
Blowing Snow9 (3.8%)
Freezing rain/drizzle7 (2.9%)
↑16.7%prior 6
Sleet, hail1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight175 (72.9%)
↑2.9%prior 170
Dark - roadway not lighted43 (17.9%)
↑43.3%prior 30
Dark - roadway lighted14 (5.8%)
↓-48.1%prior 27
Dusk5 (2.1%)
→0.0%prior 5
Dark - unknown roadway lighting2 (0.8%)
Dawn1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry150 (62.2%)
↓-3.2%prior 155
Wet29 (12.0%)
↓-6.5%prior 31
Snow27 (11.2%)
↑28.6%prior 21
Ice/frost18 (7.5%)
↑80.0%prior 10
Gravel10 (4.1%)
↓-47.4%prior 19
Slush7 (2.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

Chevrolet and Ford vehicles were involved in the most crashes in both years, with their rankings swapping. In 2018, Chevrolet-branded vehicles were most frequent (134 combined), whereas Ford was most frequent in 2017 (97). An analysis of persons involved in crashes reveals a substantial increase in the 65+ age group, which grew from 51 individuals in 2017 to 80 in 2018. The number of people involved from the 21-25 age group also rose from 60 to 79.

Top Vehicle Makes (531 vehicles)

1
CHEV93 (17.5%)
↑32.9%prior 70
2
FORD85 (16%)
↓-12.4%prior 97
3
CHEVROLET41 (7.7%)
↑10.8%prior 37
4
TOYT35 (6.6%)
↑40.0%prior 25
5
DODG20 (3.8%)
↑17.6%prior 17
6
BUIC18 (3.4%)
↑100.0%prior 9
7
JEEP18 (3.4%)
↑5.9%prior 17
8
CHRY16 (3%)
↑45.5%prior 11
9
GMC16 (3%)
↓-20.0%prior 20
10
NISS15 (2.8%)
↑150.0%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (395 persons with recorded sex)

Male225 (57.0%)
↑14.2%prior 197
Female170 (43.0%)
↓-8.1%prior 185

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 357
  • Total persons involved: 647
  • Total vehicles involved: 531

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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