Yearly Traffic Safety Analysis

136 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, O'Brien County recorded 136 total crashes, a 23.2% decrease from the 177 crashes reported in 2019. While the number of fatalities remained constant at 3 for both years, total injuries fell by 15.2%, from 79 to 67. The most significant change was the overall reduction in collision events, particularly those attributed to failure to yield from a stop sign, which decreased by over 60%.

136

▼ -23.2%was 177

Total Crash Events

3

Persons Killed

67

▼ -15.2%was 79

Persons Injured

3

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

Trend Summary

Overall, traffic crashes in O'Brien County saw a significant downward trend from 2019 to 2020. The total number of crashes decreased by 23.2%, from 177 to 136. This decline was accompanied by a 15.2% reduction in total injuries, which fell from 79 to 67, while fatalities held steady at 3 in both years.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 3→0.0%

67

Motorists Injured

Prior: 76↓-11.8%

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 timing of crashes showed some shifts between the two years, with the peak day for collisions moving from Tuesday (40 crashes) in 2019 to Thursday (28 crashes) in 2020. However, the peak hour for crashes remained consistent at 3 p.m. in both periods, with 16 incidents in 2019 and 14 in 2020. Notably, crashes on Tuesdays decreased by 55% year-over-year, while Wednesday crashes increased from 14 to 26.

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

While the number of fatal crashes remained constant at 3 for both years, the fatal crash rate increased from 1.7% in 2019 to 2.2% in 2020 due to the lower overall crash volume. The proportion of crashes resulting in any type of injury was stable, at 34.4% in 2019 and 35.3% in 2020. However, the count of serious injury crashes more than doubled from 2 to 5, representing a rise in share from 1.1% to 3.7% of all crashes.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2.2%
→0.0%prior 3
Serious Injury5serious injury crashes3.7%
↑150.0%prior 2
Minor Injury19minor injury crashes14%
↓-34.5%prior 29
Possible Injury24possible injury crashes17.6%
↓-20.0%prior 30
No Injury85no injury crashes62.5%
↓-24.8%prior 113

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

"Driving too fast for conditions" was the leading contributing factor in both periods, though its count decreased slightly from 27 crashes in 2019 to 25 in 2020. A significant year-over-year change was observed in crashes attributed to "FTYROW: From stop sign," which fell from 18 incidents in 2019 to 7 incidents in 2020, a 61.1% decrease in count. Conversely, crashes involving "Followed too close" increased from 8 incidents in 2019 to 12 in 2020, rising to become the second-most common factor.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions25 (18.4%)↓-7.4%prior 27
Followed too close12 (8.8%)↑50.0%prior 8
Lost Control11 (8.1%)↓-31.3%prior 16
Ran off road - left9 (6.6%)↓-40.0%prior 15
Animal8 (5.9%)↓-46.7%prior 15
FTYROW: From stop sign7 (5.1%)↓-61.1%prior 18
Driver Distraction: Other interior distraction7 (5.1%)
Ran Stop Sign6 (4.4%)
Improper Backing5 (3.7%)↓-50.0%prior 10
Made improper turn5 (3.7%)↓-16.7%prior 6

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

Road & Environmental Conditions

The distribution of crashes across different weather and lighting conditions remained largely consistent year-over-year, with a majority of incidents in both periods occurring in clear weather and during daylight hours. However, there was a notable shift in road surface conditions. The proportion of crashes on dry roads increased from 48.6% of all crashes in 2019 to 61.0% in 2020, while crashes on roads with ice or frost decreased from 27 incidents in 2019 to 14 in 2020.

Weather

Clear77 (59.2%)
↓-20.6%prior 97
Cloudy25 (19.2%)
↓-32.4%prior 37
Snow12 (9.2%)
↓-14.3%prior 14
Blowing Snow6 (4.6%)
→0.0%prior 6
Freezing rain/drizzle4 (3.1%)
Fog, smoke, smog3 (2.3%)
↓-40.0%prior 5
Severe Winds2 (1.5%)
Rain1 (0.8%)
↓-85.7%prior 7

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

Lighting

Daylight84 (64.1%)
↓-23.6%prior 110
Dark - roadway not lighted23 (17.6%)
↓-36.1%prior 36
Dark - roadway lighted16 (12.2%)
↑14.3%prior 14
Dusk5 (3.8%)
Dawn3 (2.3%)

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

Road Surface

Dry83 (63.4%)
↓-3.5%prior 86
Snow21 (16.0%)
↓-22.2%prior 27
Ice/frost14 (10.7%)
↓-48.1%prior 27
Gravel6 (4.6%)
Wet5 (3.8%)
↓-72.2%prior 18
Slush2 (1.5%)

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

Vehicles & Demographics

The primary vehicle makes involved in crashes were consistent across both periods, with Chevrolet, Ford, and Dodge vehicles being the most common in both 2019 and 2020. While the total number of vehicles involved in crashes decreased, the count of Ford vehicles rose from 38 to 45. In terms of persons involved, the representation of the 16-20 age group increased from 12.0% of all persons in 2019 to 15.1% in 2020. The share of most other age groups remained relatively stable year-over-year.

Top Vehicle Makes (214 vehicles)

1
FORD45 (21%)
↑18.4%prior 38
2
CHEV30 (14%)
↓-33.3%prior 45
3
CHEVROLET24 (11.2%)
↑26.3%prior 19
4
DODG14 (6.5%)
→0.0%prior 14
5
GMC14 (6.5%)
→0.0%prior 14
6
TOYT9 (4.2%)
↑80.0%prior 5
7
BUIC8 (3.7%)
↓-33.3%prior 12
8
FREIGHTLINER7 (3.3%)
→0.0%prior 7
9
CHRY6 (2.8%)
↓-50.0%prior 12
10
BUICK4 (1.9%)
↓-50.0%prior 8

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

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

Sex Distribution (201 persons with recorded sex)

Male126 (62.7%)
↓-15.4%prior 149
Female75 (37.3%)
↓-33.0%prior 112

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: 136
  • Total persons involved: 299
  • Total vehicles involved: 214

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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