Yearly Traffic Safety Analysis

326 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Fayette County, a total of 326 crashes were recorded in 2016, a 2.7% decrease from the 335 crashes in 2015. While total fatalities remained unchanged at two, the most notable year-over-year shift was a significant 30.3% reduction in total injuries, which fell from 109 in 2015 to 76 in 2016. Crashes involving suspected DUIs also saw a substantial drop, from 19 incidents to 11.

326

▼ -2.7%was 335

Total Crash Events

2

Persons Killed

76

▼ -30.3%was 109

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 traffic crash trends in Fayette County showed a slight improvement from 2015 to 2016. The total number of crashes decreased by 2.7%, from 335 to 326. More significantly, the number of people injured in these incidents declined by 30.3%, from 109 to 76, while the number of fatalities held steady at two for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

2

Pedestrians Injured

Prior: 0→%

74

Motorists Injured

Prior: 107↓-30.8%

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 timing of crashes showed some shifts between the two periods. In 2016, the peak days for crashes were Tuesday and Friday, each with 54 incidents, a change from 2015 when Saturday was the peak day with 57 crashes. However, the evening commute hour remained the most common time for a crash, with the 5 p.m. hour being the peak in both 2016 (28 crashes) and 2015 (27 crashes). December was the month with the highest crash volume in both years, with 52 crashes in 2016 and 51 in 2015.

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 levels improved from 2015 to 2016, although the number of fatal crashes remained constant at two, representing 0.6% of all crashes in both years. The proportion of crashes resulting in any level of injury decreased from 23.6% in 2015 (79 crashes) to 18.5% in 2016 (60 crashes). This was driven by a drop in serious injury crashes from 17 to 10 and minor injury crashes from 37 to 27. Consequently, the share of no-injury crashes rose from 75.8% to 81.0% year-over-year.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
→0.0%prior 2
Serious Injury10serious injury crashes3.1%
↓-41.2%prior 17
Minor Injury27minor injury crashes8.3%
↓-27.0%prior 37
Possible Injury23possible injury crashes7.1%
↓-8.0%prior 25
No Injury264no injury crashes81%
↑3.9%prior 254

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 years, with a nearly identical count of 127 crashes in 2016 compared to 128 in 2015. While the top factor was stable, there were significant shifts in other driver-related behaviors. Crashes attributed to 'Driving too fast for conditions' increased by 64.7% in count, rising from 17 incidents in 2015 to 28 in 2016. Conversely, crashes due to 'Followed too close' decreased by 63.6% (from 11 to 4), and incidents of 'Ran Stop Sign' fell by 75% (from 8 to 2).

Officer-Reported Primary Contributing Cause

Animal127 (39%)↓-0.8%prior 128
Lost Control30 (9.2%)↓-9.1%prior 33
Driving too fast for conditions28 (8.6%)↑64.7%prior 17
Ran off road - straight20 (6.1%)↑17.6%prior 17
FTYROW: From stop sign14 (4.3%)↑55.6%prior 9
Other (explain in narrative): Other14 (4.3%)↑27.3%prior 11
Ran off road - left12 (3.7%)↑71.4%prior 7
Driver Distraction: Other interior distraction11 (3.4%)↑57.1%prior 7
Operating vehicle in an reckless, erratic, careless, negligent manner7 (2.1%)
FTYROW: Making left turn7 (2.1%)↓-36.4%prior 11

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

Road & Environmental Conditions

There was a noticeable shift in the environmental conditions under which crashes occurred. In 2016, there were more crashes on snowy and icy roads (51 combined) compared to 2015 (38 combined). Conversely, crashes on wet roads decreased significantly from 29 in 2015 to 13 in 2016. The proportion of crashes happening in daylight decreased slightly from 40.9% in 2015 to 38.3% in 2016, while crashes in dark, unlighted conditions remained relatively stable with 62 incidents in 2016 versus 59 in 2015.

Weather

Clear130 (59.6%)
↓-9.1%prior 143
Cloudy39 (17.9%)
↓-11.4%prior 44
Snow22 (10.1%)
↑29.4%prior 17
Blowing Snow8 (3.7%)
Freezing rain/drizzle6 (2.8%)
Rain6 (2.8%)
↓-64.7%prior 17
Fog, smoke, smog5 (2.3%)
Other (explain in narrative)2 (0.9%)

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

Lighting

Daylight125 (57.3%)
↓-8.8%prior 137
Dark - roadway not lighted62 (28.4%)
↑5.1%prior 59
Dawn12 (5.5%)
↑71.4%prior 7
Dark - roadway lighted12 (5.5%)
↓-42.9%prior 21
Dusk6 (2.8%)
↓-14.3%prior 7
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry131 (60.1%)
↓-14.4%prior 153
Snow28 (12.8%)
↑33.3%prior 21
Ice/frost23 (10.6%)
↑35.3%prior 17
Gravel15 (6.9%)
↑15.4%prior 13
Wet13 (6.0%)
↓-55.2%prior 29
Slush7 (3.2%)
Sand1 (0.5%)

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

Vehicles & Demographics

The makes of vehicles most frequently involved in crashes remained consistent, with Ford (84 in 2016 vs. 83 in 2015) and Chevrolet (103 vs. 104) showing stable numbers year-over-year. In terms of driver demographics, there was a significant decrease in the number of younger people involved in crashes. The count of persons aged 16-20 involved in crashes dropped from 88 in 2015 to 62 in 2016, and the 21-25 age group saw a similar decline from 80 persons to 62.

Top Vehicle Makes (422 vehicles)

1
FORD84 (19.9%)
↑1.2%prior 83
2
CHEVROLET52 (12.3%)
↑18.2%prior 44
3
CHEV51 (12.1%)
↓-15.0%prior 60
4
DODG26 (6.2%)
↑44.4%prior 18
5
DODGE14 (3.3%)
↓-53.3%prior 30
6
PONTIAC13 (3.1%)
↓-7.1%prior 14
7
NISSAN11 (2.6%)
8
JEEP10 (2.4%)
↑42.9%prior 7
9
BUIC10 (2.4%)
→0.0%prior 10
10
PONT10 (2.4%)
↓-44.4%prior 18

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

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

Sex Distribution (325 persons with recorded sex)

Male182 (56.0%)
↓-25.4%prior 244
Female143 (44.0%)
↓-20.1%prior 179

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: 326
  • Total persons involved: 475
  • Total vehicles involved: 422

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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