Yearly Traffic Safety Analysis

755 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Webster County, total vehicle crashes decreased by 14.2% from 880 in 2019 to 755 in 2020. During this period, total fatalities fell from 10 to 3, and injuries dropped from 214 to 187. One of the most notable year-over-year shifts was a 94.1% increase in crashes involving a driver under the influence (DUI), which rose from 17 incidents in 2019 to 33 in 2020.

755

▼ -14.2%was 880

Total Crash Events

3

▼ -70.0%was 10

Persons Killed

187

▼ -12.6%was 214

Persons Injured

3

▼ -66.7%was 9

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, Webster County saw a downward trend in traffic incidents between 2019 and 2020. Total crashes fell by 125, from 880 to 755, representing a 14.2% year-over-year reduction. This trend extended to crash outcomes, with total fatalities decreasing from 10 to 3 and total injuries declining from 214 to 187.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

2

Motorists Killed

Prior: 10↓-80.0%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 2→0.0%

3

Cyclists Injured

Prior: 3→0.0%

181

Motorists Injured

Prior: 209↓-13.4%

1

Other Injured

Prior: 0→%

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 peak day for crashes shifted from Wednesday (149 crashes) in 2019 to Friday (120 crashes) in 2020. The 5 p.m. hour remained the peak time for collisions in both periods, though the number of crashes during this hour decreased from 74 to 57 year-over-year. The morning commute peak at 7 a.m. was also less pronounced in 2020, with 34 crashes compared to 53 in the prior year.

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 rate of fatal crashes decreased from 1.0% of all incidents in 2019 to 0.4% in 2020, as total fatalities dropped from 10 to 3. Conversely, the share of crashes resulting in serious injuries increased significantly, rising from 0.7% (6 crashes) in the prior period to 2.3% (17 crashes) in the current period. Crashes resulting in no injury accounted for 76.4% of incidents in 2020, a slight decrease from 77.8% in 2019.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.4%
↓-66.7%prior 9
Serious Injury17serious injury crashes2.3%
↑183.3%prior 6
Minor Injury42minor injury crashes5.6%
↓-2.3%prior 43
Possible Injury116possible injury crashes15.4%
↓-15.3%prior 137
No Injury577no injury crashes76.4%
↓-15.8%prior 685

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 involving an animal remained the top contributing factor in both periods, though the count of such incidents decreased from 123 in 2019 to 109 in 2020. Crashes attributed to "Driving too fast for conditions" also declined from 53 to 47. A significant reduction was seen in crashes involving "Failure to yield from a stop sign," which fell by 34.6% from 52 incidents in 2019 to 34 in 2020.

Officer-Reported Primary Contributing Cause

Animal109 (14.4%)↓-11.4%prior 123
Other (explain in narrative): Other69 (9.1%)↓-34.9%prior 106
Driving too fast for conditions47 (6.2%)↓-11.3%prior 53
Lost Control44 (5.8%)↑2.3%prior 43
Followed too close38 (5%)↓-11.6%prior 43
FTYROW: From stop sign34 (4.5%)↓-34.6%prior 52
Driver Distraction: Other interior distraction32 (4.2%)↑14.3%prior 28
Ran off road - left30 (4%)↓-18.9%prior 37
FTYROW: Making left turn30 (4%)↑3.4%prior 29
Other (explain in narrative): No improper action20 (2.6%)↑25.0%prior 16

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 proportion of crashes occurring on dry road surfaces increased from 51.6% in 2019 to 67.0% in 2020. Concurrently, crashes on adverse surfaces saw a significant year-over-year decrease; incidents on snowy roads fell from 113 to 53, and crashes on wet roads were reduced from 105 to 51. While the majority of crashes in both years occurred in daylight, crashes in darkness on lighted roadways increased from 81 to 104 incidents.

Weather

Clear466 (70.5%)
↓-0.6%prior 469
Cloudy128 (19.4%)
↓-30.4%prior 184
Snow32 (4.8%)
↓-3.0%prior 33
Rain19 (2.9%)
↓-54.8%prior 42
Fog, smoke, smog5 (0.8%)
Blowing Snow5 (0.8%)
↓-61.5%prior 13
Freezing rain/drizzle4 (0.6%)
↓-69.2%prior 13
Severe Winds2 (0.3%)

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

Lighting

Daylight443 (66.2%)
↓-18.4%prior 543
Dark - roadway lighted104 (15.5%)
↑28.4%prior 81
Dark - roadway not lighted86 (12.9%)
↓-14.9%prior 101
Dawn17 (2.5%)
↑30.8%prior 13
Dusk15 (2.2%)
↓-34.8%prior 23
Dark - unknown roadway lighting4 (0.6%)

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

Road Surface

Dry506 (76.4%)
↑11.5%prior 454
Snow53 (8.0%)
↓-53.1%prior 113
Wet51 (7.7%)
↓-51.4%prior 105
Ice/frost38 (5.7%)
↓-51.3%prior 78
Slush8 (1.2%)
↑14.3%prior 7
Gravel3 (0.5%)
↓-40.0%prior 5
Mud, dirt3 (0.5%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved in crashes in both 2019 and 2020, with the count for both makes decreasing year-over-year. The number of people involved in crashes also declined across most age demographics. Notably, the 26-34 age group saw a reduction from 298 individuals involved in 2019 to 200 in 2020, and the 65+ age group saw a similar drop from 215 to 166 individuals.

Top Vehicle Makes (1,253 vehicles)

1
FORD208 (16.6%)
↓-16.1%prior 248
2
CHEV192 (15.3%)
↓-25.6%prior 258
3
CHEVROLET97 (7.7%)
↑10.2%prior 88
4
NR59 (4.7%)
↓-4.8%prior 62
5
DODG49 (3.9%)
↓-45.6%prior 90
6
JEEP46 (3.7%)
↓-14.8%prior 54
7
GMC46 (3.7%)
↓-29.2%prior 65
8
BUIC45 (3.6%)
↓-6.3%prior 48
9
CHRY41 (3.3%)
↓-19.6%prior 51
10
DODGE41 (3.3%)
↑17.1%prior 35

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

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

Sex Distribution (1,059 persons with recorded sex)

Male640 (60.4%)
↓-4.8%prior 672
Female419 (39.6%)
↓-33.1%prior 626

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: 755
  • Total persons involved: 1,675
  • Total vehicles involved: 1,253

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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