Yearly Traffic Safety Analysis

289 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Fayette County, there were 289 total crashes in 2020, a 2.0% decrease from the 295 crashes recorded in 2019. This period also saw a 15.7% decrease in total injuries, from 89 to 75, and a drop in fatalities from five to four. The most significant year-over-year change was a 65.2% reduction in crashes occurring on icy or frosty road surfaces, which fell from 46 incidents in 2019 to 12 in 2020.

289

▼ -2.0%was 295

Total Crash Events

4

▼ -20.0%was 5

Persons Killed

75

▼ -15.7%was 89

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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in traffic crashes in Fayette County shows a slight decline from 2019 to 2020. Total crashes decreased by 2.0% from 295 to 289. Correspondingly, the number of people injured fell by 15.7% from 89 to 75, and fatalities decreased from 5 to 4.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 5↓-20.0%

75

Motorists Injured

Prior: 86↓-12.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained largely consistent year-over-year. Monday was the day with the most crashes in both 2020 (56 crashes) and 2019 (53 crashes). The peak hour for collisions saw a minor shift, moving from 5 p.m. in 2019 (25 crashes) to 6 p.m. in 2020 (24 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes showed mixed changes between the two periods. The number of fatal crashes remained constant at four, with the fatal crash rate per 100 crashes holding steady at 1.38 in 2020 compared to 1.36 in 2019. The count of serious injury crashes increased slightly from seven to eight, while crashes involving possible injuries decreased from 25 to 21. Crashes resulting in no injury accounted for a nearly identical proportion of all incidents, at 78.9% in 2020 versus 79.0% in 2019.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.4%
→0.0%prior 4
Serious Injury8serious injury crashes2.8%
↑14.3%prior 7
Minor Injury28minor injury crashes9.7%
↑7.7%prior 26
Possible Injury21possible injury crashes7.3%
↓-16.0%prior 25
No Injury228no injury crashes78.9%
↓-2.1%prior 233

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals were the leading contributing factor in both years, with the count of such incidents increasing by 14.6% from 96 in 2019 to 110 in 2020. In contrast, crashes attributed to 'Lost Control' decreased by 34.4%, from 32 to 21 incidents. Similarly, crashes where 'Driving too fast for conditions' was a factor saw a significant 53.8% drop in count, from 26 in 2019 to 12 in 2020.

Officer-Reported Primary Contributing Cause

Animal110 (38.1%)↑14.6%prior 96
Lost Control21 (7.3%)↓-34.4%prior 32
Ran off road - left19 (6.6%)↑26.7%prior 15
FTYROW: From stop sign17 (5.9%)↑21.4%prior 14
Other (explain in narrative): Other14 (4.8%)↓-12.5%prior 16
Driving too fast for conditions12 (4.2%)↓-53.8%prior 26
Ran Stop Sign10 (3.5%)↓-28.6%prior 14
Ran off road - straight9 (3.1%)↓-18.2%prior 11
Operating vehicle in an reckless, erratic, careless, negligent manner8 (2.8%)
Followed too close7 (2.4%)↓-12.5%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

There was a notable shift in crash conditions year-over-year, primarily related to road surface. Crashes on icy or frosty roads decreased by 65.2%, from 46 incidents in 2019 to just 12 in 2020. Consequently, the proportion of crashes on dry roads increased from 38.0% of all crashes in 2019 to 44.6% in 2020. The distribution of crashes by lighting condition and weather remained relatively stable, with 'Clear' weather and 'Daylight' conditions being the most common settings in both years.

Weather

Clear129 (68.6%)
↓-0.8%prior 130
Cloudy34 (18.1%)
↓-12.8%prior 39
Snow9 (4.8%)
↓-25.0%prior 12
Rain7 (3.7%)
→0.0%prior 7
Freezing rain/drizzle6 (3.2%)
↓-50.0%prior 12
Blowing Snow3 (1.6%)
↓-62.5%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight134 (71.3%)
↓-7.6%prior 145
Dark - roadway not lighted34 (18.1%)
↓-26.1%prior 46
Dark - roadway lighted10 (5.3%)
↓-9.1%prior 11
Dusk7 (3.7%)
→0.0%prior 7
Dawn2 (1.1%)
Dark - unknown roadway lighting1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry129 (68.3%)
↑15.2%prior 112
Snow17 (9.0%)
↓-22.7%prior 22
Wet15 (7.9%)
↑15.4%prior 13
Gravel14 (7.4%)
↑7.7%prior 13
Ice/frost12 (6.3%)
↓-73.9%prior 46
Slush2 (1.1%)
↓-60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (402 vehicles)

1
CHEV97 (24.1%)
↑36.6%prior 71
2
FORD75 (18.7%)
↓-19.4%prior 93
3
CHEVROLET31 (7.7%)
↑24.0%prior 25
4
GMC19 (4.7%)
↓-9.5%prior 21
5
DODG15 (3.7%)
↓-28.6%prior 21
6
JEEP13 (3.2%)
↓-7.1%prior 14
7
BUIC12 (3%)
↑20.0%prior 10
8
DODGE8 (2%)
↓-27.3%prior 11
9
CHRYSLER8 (2%)
↑14.3%prior 7
10
PONT8 (2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (375 persons with recorded sex)

Male236 (62.9%)
↑9.8%prior 215
Female139 (37.1%)
↓-18.2%prior 170

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 289
  • Total persons involved: 606
  • Total vehicles involved: 402

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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