Yearly Traffic Safety Analysis

265 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Carroll County, total traffic crashes decreased slightly from 269 in 2015 to 265 in 2016, a change of approximately 1.5%. While the overall crash volume remained stable, the most significant year-over-year change was the increase in traffic-related fatalities, which rose from zero in 2015 to four in 2016.

265

▼ -1.5%was 269

Total Crash Events

4

Persons Killed

112

▲ 12.0%was 100

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

Overall crash volume in Carroll County remained relatively stable, with a slight decrease of 4 incidents from 269 in 2015 to 265 in 2016. However, the severity of these crashes increased, as total injuries rose by 12% from 100 to 112, and fatalities increased from zero to four.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

3

Motorists Killed

Prior: 0→%

2

Pedestrians Injured

Prior: 3↓-33.3%

1

Cyclists Injured

Prior: 1→0.0%

109

Motorists Injured

Prior: 96↑13.5%

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 between the two periods. The day with the highest number of crashes moved from Tuesday (51 crashes) in 2015 to Friday (56 crashes) in 2016. While the 3 p.m. hour was a peak time in both years, its dominance decreased from 29 crashes in 2015 to 22 in 2016, with the 12 p.m. and 1 p.m. hours also recording 22 crashes each in the more recent period.

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

Crash severity notably increased in 2016, with 4 fatal crashes recorded, compared to none in the prior year. The proportion of crashes resulting in serious injuries also saw a slight rise, from 3.0% in 2015 to 3.8% in 2016. Conversely, the share of crashes with possible injuries decreased from 14.5% to 11.7%, and no-injury crashes fell from 71.0% to 69.8% of all incidents.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.5%
Serious Injury10serious injury crashes3.8%
↑25.0%prior 8
Minor Injury35minor injury crashes13.2%
↑12.9%prior 31
Possible Injury31possible injury crashes11.7%
↓-20.5%prior 39
No Injury185no injury crashes69.8%
↓-3.1%prior 191

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

The leading contributing factors for crashes shifted year-over-year. 'Ran off road - left' became the most common factor in 2016 with 32 incidents, up from 26 in 2015. The previous top factor, 'Failure to yield from a stop sign,' decreased from 27 to 20 incidents. Notably, crashes attributed to 'Driving too fast for conditions' increased by over 61% in count, from 13 incidents in 2015 to 21 in 2016. Conversely, incidents involving 'Followed too close' dropped from 17 to 13.

Officer-Reported Primary Contributing Cause

Ran off road - left32 (12.1%)↑23.1%prior 26
Driving too fast for conditions21 (7.9%)↑61.5%prior 13
FTYROW: From stop sign20 (7.5%)↓-25.9%prior 27
FTYROW: Making left turn19 (7.2%)↑35.7%prior 14
Lost Control17 (6.4%)↑13.3%prior 15
Other (explain in narrative): Other17 (6.4%)→0.0%prior 17
Ran Stop Sign15 (5.7%)↑25.0%prior 12
Followed too close13 (4.9%)↓-23.5%prior 17
Improper Backing10 (3.8%)↓-9.1%prior 11
Other (explain in narrative): No improper action9 (3.4%)↑28.6%prior 7

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

Road & Environmental Conditions

Crashes were more likely to occur on dry roads in 2016 compared to the prior year, with the proportion of such incidents rising from 64.3% to 70.9%. Correspondingly, the share of crashes on adverse road surfaces like wet, snow, or ice decreased from 29.7% in 2015 to 21.9% in 2016. A similar trend was observed in weather conditions, where crashes during clear weather increased while those in adverse weather conditions declined. The proportion of crashes occurring in daylight remained stable, accounting for roughly three-quarters of all incidents in both years.

Weather

Clear175 (67.6%)
↑3.6%prior 169
Cloudy51 (19.7%)
↑18.6%prior 43
Snow14 (5.4%)
↓-36.4%prior 22
Rain5 (1.9%)
↓-77.3%prior 22
Blowing Snow5 (1.9%)
Freezing rain/drizzle5 (1.9%)
→0.0%prior 5
Fog, smoke, smog3 (1.2%)
Sleet, hail1 (0.4%)

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

Lighting

Daylight201 (77.0%)
↑2.6%prior 196
Dark - roadway not lighted25 (9.6%)
↓-26.5%prior 34
Dark - roadway lighted25 (9.6%)
↑31.6%prior 19
Dawn4 (1.5%)
↓-50.0%prior 8
Dusk4 (1.5%)
Dark - unknown roadway lighting2 (0.8%)

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

Road Surface

Dry188 (72.3%)
↑8.7%prior 173
Ice/frost19 (7.3%)
↑58.3%prior 12
Snow18 (6.9%)
↓-35.7%prior 28
Wet17 (6.5%)
↓-55.3%prior 38
Gravel13 (5.0%)
↑30.0%prior 10
Slush4 (1.5%)
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 Ford and Chevrolet vehicles involved in crashes decreased year-over-year; Ford vehicles dropped from 93 to 58, and aggregated Chevrolet models from 123 to 112. Despite these decreases, they remained the most common vehicle makes in collisions. Analysis of persons involved shows a shift in age demographics, with the 16-20 age group increasing from 70 to 81 individuals. Conversely, the number of persons in the 21-25 age group involved in crashes fell from 81 to 58.

Top Vehicle Makes (469 vehicles)

1
CHEV61 (13%)
↓-18.7%prior 75
2
FORD58 (12.4%)
↓-37.6%prior 93
3
CHEVROLET51 (10.9%)
↑6.3%prior 48
4
GMC25 (5.3%)
↓-7.4%prior 27
5
PONT21 (4.5%)
↑40.0%prior 15
6
DODG17 (3.6%)
↓-22.7%prior 22
7
TOYT16 (3.4%)
↓-11.1%prior 18
8
BUIC15 (3.2%)
↑25.0%prior 12
9
DODGE15 (3.2%)
↓-11.8%prior 17
10
TOYOTA14 (3%)
↑7.7%prior 13

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

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

Sex Distribution (363 persons with recorded sex)

Male194 (53.4%)
↓-18.8%prior 239
Female169 (46.6%)
↓-12.9%prior 194

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: 265
  • Total persons involved: 533
  • Total vehicles involved: 469

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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