Yearly Traffic Safety Analysis

328 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Fayette County, total traffic crashes remained nearly stable, with 328 incidents in 2017 compared to 326 in 2016, an increase of less than 1%. Despite the stable crash volume, the severity of outcomes worsened. The number of people injured rose by 22.4% from 76 to 93, and total fatalities increased from 2 to 3 year-over-year.

328

▲ 0.6%was 326

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

93

▲ 22.4%was 76

Persons Injured

3

▲ 50.0%was 2

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 Fayette County was stable between 2016 and 2017, increasing by only two incidents from 326 to 328. However, this stability in total crashes masks a negative trend in outcomes, as total injuries rose from 76 to 93 (+22.4%) and fatalities increased from 2 to 3.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 2↑50.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

92

Motorists Injured

Prior: 74↑24.3%

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 showed some shifts between the two periods. In 2017, the peak days for crashes were Tuesday and Saturday, each with 56 incidents. This is a change from 2016, when Tuesday and Friday were the peak days with 54 crashes each. The peak hour also shifted later, from 5 p.m. in 2016 (28 crashes) to 6 p.m. in 2017 (37 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 increased in 2017 compared to the prior year. The number of fatal crashes rose from 2 to 3, and the fatality rate per 100 crashes increased from 0.61 to 0.91. While the count of minor injury crashes was unchanged at 27, crashes resulting in serious injuries increased from 10 to 12, and those with possible injuries rose from 23 to 31.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.9%
↑50.0%prior 2
Serious Injury12serious injury crashes3.7%
↑20.0%prior 10
Minor Injury27minor injury crashes8.2%
→0.0%prior 27
Possible Injury31possible injury crashes9.5%
↑34.8%prior 23
No Injury255no injury crashes77.7%
↓-3.4%prior 264

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 top contributing factor in both years, though the count decreased by 10.2% from 127 crashes in 2016 to 114 in 2017. 'Lost Control' was the second-ranked factor in both periods with an identical count of 30 crashes. Notably, crashes attributed to 'Followed too close' increased by 225% in count, from 4 to 13 incidents, while those involving 'Driving too fast for conditions' fell by 39.3% in count, from 28 to 17.

Officer-Reported Primary Contributing Cause

Animal114 (34.8%)↓-10.2%prior 127
Lost Control30 (9.1%)→0.0%prior 30
Other (explain in narrative): Other25 (7.6%)↑78.6%prior 14
Ran off road - straight23 (7%)↑15.0%prior 20
Driving too fast for conditions17 (5.2%)↓-39.3%prior 28
Followed too close13 (4%)
FTYROW: From stop sign12 (3.7%)↓-14.3%prior 14
Ran off road - left9 (2.7%)↓-25.0%prior 12
Driver Distraction: Inattentive/lost in thought9 (2.7%)
Operating vehicle in an reckless, erratic, careless, negligent manner8 (2.4%)↑14.3%prior 7

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

Road & Environmental Conditions

Year-over-year, there was a shift in the conditions under which crashes occurred. In 2017, more crashes happened in clear weather (149 vs. 130) and on dry roads (145 vs. 131) compared to 2016. Conversely, crashes during snowy weather decreased from 22 to 7, and crashes on snowy road surfaces fell from 28 to 10. However, incidents on wet roads more than doubled, increasing from 13 in 2016 to 32 in 2017.

Weather

Clear149 (65.1%)
↑14.6%prior 130
Cloudy42 (18.3%)
↑7.7%prior 39
Rain13 (5.7%)
↑116.7%prior 6
Freezing rain/drizzle9 (3.9%)
↑50.0%prior 6
Snow7 (3.1%)
↓-68.2%prior 22
Fog, smoke, smog6 (2.6%)
↑20.0%prior 5
Blowing Snow2 (0.9%)
↓-75.0%prior 8
Severe Winds1 (0.4%)

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

Lighting

Daylight148 (64.9%)
↑18.4%prior 125
Dark - roadway not lighted54 (23.7%)
↓-12.9%prior 62
Dark - roadway lighted16 (7.0%)
↑33.3%prior 12
Dusk6 (2.6%)
→0.0%prior 6
Dawn2 (0.9%)
↓-83.3%prior 12
Dark - unknown roadway lighting2 (0.9%)

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

Road Surface

Dry145 (63.3%)
↑10.7%prior 131
Wet32 (14.0%)
↑146.2%prior 13
Ice/frost31 (13.5%)
↑34.8%prior 23
Snow10 (4.4%)
↓-64.3%prior 28
Gravel8 (3.5%)
↓-46.7%prior 15
Sand1 (0.4%)
Slush1 (0.4%)
↓-85.7%prior 7
Water (standing or moving)1 (0.4%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes, Ford and Chevrolet, remained consistent across both years, though their counts shifted. The number of persons from younger age groups involved in crashes saw an increase; involvement for the 16-20 age group rose from 62 to 73 individuals, and for the 21-25 age group, it rose from 62 to 72. Meanwhile, involvement for the 45-54 age group decreased from 79 to 64 persons.

Top Vehicle Makes (452 vehicles)

1
FORD79 (17.5%)
↓-6.0%prior 84
2
CHEV78 (17.3%)
↑52.9%prior 51
3
CHEVROLET46 (10.2%)
↓-11.5%prior 52
4
BUIC20 (4.4%)
↑100.0%prior 10
5
CHRY19 (4.2%)
↑111.1%prior 9
6
PONT18 (4%)
↑80.0%prior 10
7
DODG17 (3.8%)
↓-34.6%prior 26
8
DODGE15 (3.3%)
↑7.1%prior 14
9
GMC13 (2.9%)
↑44.4%prior 9
10
CHRYSLER11 (2.4%)
↑57.1%prior 7

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

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

Sex Distribution (317 persons with recorded sex)

Male176 (55.5%)
↓-3.3%prior 182
Female141 (44.5%)
↓-1.4%prior 143

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: 328
  • Total persons involved: 522
  • Total vehicles involved: 452

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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