Yearly Traffic Safety Analysis

265 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Carroll County, the total number of traffic crashes was unchanged year-over-year, with 265 incidents recorded in both 2017 and 2016. Despite the stable crash volume, key safety outcomes improved, with total fatalities decreasing from 4 to 3 and total injuries declining from 112 to 96. A notable shift occurred in contributing factors, where 'Failure to yield from a stop sign' rose to become the leading cause in 2017, with its incident count increasing by 45% from the prior year.

265

Total Crash Events

3

▼ -25.0%was 4

Persons Killed

96

▼ -14.3%was 112

Persons Injured

3

▼ -25.0%was 4

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume in Carroll County remained stable between 2016 and 2017, holding at 265 incidents in both periods. However, the severity of these incidents lessened, as total fatalities fell by 25% from 4 to 3, and the number of people injured decreased by 14.3% from 112 to 96.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 1↓-100.0%

3

Motorists Killed

Prior: 3→0.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

1

Cyclists Injured

Prior: 1→0.0%

94

Motorists Injured

Prior: 109↓-13.8%

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 Tuesday with 50 incidents, a change from 2016 when Friday was the peak with 56 crashes. The peak hours also changed, concentrating more in the late afternoon in 2017, with both the 3 p.m. and 5 p.m. hours recording 26 crashes each, compared to a midday peak in 2016.

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

The severity of crashes saw a slight improvement in 2017 compared to the prior year. The number of fatal crashes decreased from 4 to 3, and serious injury crashes fell from 10 to 8. While the number of minor injury crashes was unchanged at 35, the count of crashes with possible injuries increased slightly from 31 to 34. The proportion of crashes resulting in no injuries remained identical at 69.8% across both years.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.1%
↓-25.0%prior 4
Serious Injury8serious injury crashes3%
↓-20.0%prior 10
Minor Injury35minor injury crashes13.2%
→0.0%prior 35
Possible Injury34possible injury crashes12.8%
↑9.7%prior 31
No Injury185no injury crashes69.8%
→0.0%prior 185

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

The ranking of top contributing factors changed significantly year-over-year. 'Failure to yield from a stop sign' became the leading cause in 2017, with its count increasing by 45% from 20 to 29 crashes. 'Followed too close' also saw a substantial rise, growing 84.6% from 13 to 24 incidents. Conversely, 'Ran off road - left,' the top factor in 2016, saw its count fall by 31.3% from 32 to 22 crashes, and 'Driving too fast for conditions' dropped from 21 incidents to just 4.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign29 (10.9%)↑45.0%prior 20
Followed too close24 (9.1%)↑84.6%prior 13
Ran off road - left22 (8.3%)↓-31.3%prior 32
FTYROW: Making left turn21 (7.9%)↑10.5%prior 19
Animal19 (7.2%)↑171.4%prior 7
Other (explain in narrative): Other19 (7.2%)↑11.8%prior 17
Lost Control16 (6%)↓-5.9%prior 17
Ran Stop Sign12 (4.5%)↓-20.0%prior 15
Ran off road - straight8 (3%)↓-11.1%prior 9
Driver Distraction: Other interior distraction7 (2.6%)↑16.7%prior 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

There was a shift in the conditions under which crashes occurred. In 2017, there was an increase in crashes during dark conditions, rising from 52 to 62 incidents compared to 2016. In contrast, crashes on adverse road surfaces such as wet, icy, or snow-covered roads decreased from 59 to 42. Crashes occurring in clear weather increased slightly from 175 to 182.

Weather

Clear182 (70.8%)
↑4.0%prior 175
Cloudy47 (18.3%)
↓-7.8%prior 51
Rain7 (2.7%)
↑40.0%prior 5
Freezing rain/drizzle7 (2.7%)
↑40.0%prior 5
Snow6 (2.3%)
↓-57.1%prior 14
Fog, smoke, smog3 (1.2%)
Blowing Snow2 (0.8%)
↓-60.0%prior 5
Other (explain in narrative)1 (0.4%)
Severe Winds1 (0.4%)
Sleet, hail1 (0.4%)

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

Lighting

Daylight181 (70.2%)
↓-10.0%prior 201
Dark - roadway not lighted32 (12.4%)
↑28.0%prior 25
Dark - roadway lighted27 (10.5%)
↑8.0%prior 25
Dusk10 (3.9%)
Dawn5 (1.9%)
Dark - unknown roadway lighting3 (1.2%)

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

Road Surface

Dry199 (77.4%)
↑5.9%prior 188
Wet19 (7.4%)
↑11.8%prior 17
Gravel16 (6.2%)
↑23.1%prior 13
Ice/frost13 (5.1%)
↓-31.6%prior 19
Snow8 (3.1%)
↓-55.6%prior 18
Slush2 (0.8%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Chevrolet and Ford, maintained their top rankings in 2017, with both showing an increase in their total counts from the previous year. An analysis of persons involved reveals a demographic shift; the 55-64 age group saw its involvement increase from 55 individuals in 2016 to 69 in 2017. The 16-20 age group also saw a slight increase in involvement from 81 to 88 individuals.

Top Vehicle Makes (464 vehicles)

1
CHEV74 (15.9%)
↑21.3%prior 61
2
FORD65 (14%)
↑12.1%prior 58
3
CHEVROLET51 (11%)
→0.0%prior 51
4
DODG21 (4.5%)
↑23.5%prior 17
5
TOYT19 (4.1%)
↑18.8%prior 16
6
GMC18 (3.9%)
↓-28.0%prior 25
7
CHRY14 (3%)
↑100.0%prior 7
8
JEEP13 (2.8%)
↑44.4%prior 9
9
BUIC13 (2.8%)
↓-13.3%prior 15
10
TOYOTA11 (2.4%)
↓-21.4%prior 14

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 (360 persons with recorded sex)

Male198 (55.0%)
↑2.1%prior 194
Female162 (45.0%)
↓-4.1%prior 169

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: 265
  • Total persons involved: 538
  • Total vehicles involved: 464

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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