Yearly Traffic Safety Analysis

598 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Clinton County, traffic crashes decreased by 21.5% from 762 in 2019 to 598 in 2020. This downward trend included a 50% reduction in fatalities, from 8 to 4. Despite the overall decrease in collisions, the number of crashes attributed to driving under the influence (DUI) rose from 17 to 32, an 88% increase year-over-year.

598

▼ -21.5%was 762

Total Crash Events

4

▼ -50.0%was 8

Persons Killed

231

▼ -8.7%was 253

Persons Injured

3

▼ -62.5%was 8

Fatal Crash Events

Note: "Persons Killed" (4) 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 safety metrics in Clinton County showed improvement from 2019 to 2020. The total number of crashes fell by 21.5%, from 762 to 598. Correspondingly, total fatalities were halved from 8 to 4, and total injuries saw a modest decrease from 253 to 231.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 7↓-42.9%

6

Pedestrians Injured

Prior: 5↑20.0%

7

Cyclists Injured

Prior: 10↓-30.0%

218

Motorists Injured

Prior: 238↓-8.4%

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 consistent between 2019 and 2020. Friday was the peak day for crashes in both periods, with 135 incidents in 2019 and 110 in 2020. Similarly, the 3 p.m. hour was the peak time for collisions in both years, accounting for 75 crashes in 2019 and 53 in 2020. While the peak times did not shift, the volume of crashes during these periods decreased in line with the overall trend.

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 total number of fatal crashes dropped from 8 in 2019 to 3 in 2020, the distribution of injury severity shifted. The share of crashes resulting in serious injuries increased from 1.4% (11 crashes) in the prior period to 3.0% (18 crashes) in the current period. Similarly, minor injury crashes grew from 7.1% (54 crashes) to 13.9% (83 crashes) of all incidents. Conversely, the proportion of crashes with possible injuries decreased from 19.4% to 15.6%.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.5%
↓-62.5%prior 8
Serious Injury18serious injury crashes3%
↑63.6%prior 11
Minor Injury83minor injury crashes13.9%
↑53.7%prior 54
Possible Injury93possible injury crashes15.6%
↓-37.2%prior 148
No Injury401no injury crashes67.1%
↓-25.9%prior 541

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

The leading contributing factors remained broadly similar year-over-year, though their counts decreased. Collisions involving an animal were the top factor in both 2019 (107 crashes) and 2020 (89 crashes). The count of crashes due to 'Lost Control' saw a significant decrease from 64 to 34, and 'Failure to Yield Right of Way from a stop sign' incidents dropped from 59 to 38. In contrast, crashes where a driver 'Ran Stop Sign' were nearly unchanged, with 34 in 2019 and 32 in 2020.

Officer-Reported Primary Contributing Cause

Animal89 (14.9%)↓-16.8%prior 107
Followed too close48 (8%)↓-7.7%prior 52
Other (explain in narrative): Other39 (6.5%)↓-25.0%prior 52
FTYROW: From stop sign38 (6.4%)↓-35.6%prior 59
Lost Control34 (5.7%)↓-46.9%prior 64
Ran Stop Sign32 (5.4%)↓-5.9%prior 34
Ran off road - straight26 (4.3%)↓-21.2%prior 33
FTYROW: Making left turn24 (4%)↑4.3%prior 23
Ran off road - left23 (3.8%)↓-30.3%prior 33
Driving too fast for conditions23 (3.8%)↓-41.0%prior 39

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

Road & Environmental Conditions

Crashes under adverse weather and road conditions decreased notably from 2019 to 2020. Collisions in snow fell from 46 to 19, and crashes on snowy roads dropped from 63 to 11. Crashes on wet roads also declined from 91 to 50. While daylight remained the most common lighting condition, its share of crashes fell from 61.7% (470 incidents) in 2019 to 54.5% (326 incidents) in 2020.

Weather

Clear398 (75.5%)
↑0.3%prior 397
Cloudy67 (12.7%)
↓-55.9%prior 152
Rain23 (4.4%)
↓-45.2%prior 42
Snow19 (3.6%)
↓-58.7%prior 46
Freezing rain/drizzle16 (3.0%)
↑14.3%prior 14
Other (explain in narrative)2 (0.4%)
Severe Winds1 (0.2%)
Fog, smoke, smog1 (0.2%)
↓-80.0%prior 5

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

Lighting

Daylight326 (61.5%)
↓-30.6%prior 470
Dark - roadway not lighted102 (19.2%)
↑6.3%prior 96
Dark - roadway lighted74 (14.0%)
↓-7.5%prior 80
Dusk14 (2.6%)
↓-12.5%prior 16
Dawn11 (2.1%)
↓-26.7%prior 15
Dark - unknown roadway lighting3 (0.6%)

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

Road Surface

Dry411 (77.5%)
↓-8.5%prior 449
Wet50 (9.4%)
↓-45.1%prior 91
Ice/frost29 (5.5%)
↓-42.0%prior 50
Gravel19 (3.6%)
↑90.0%prior 10
Snow11 (2.1%)
↓-82.5%prior 63
Slush8 (1.5%)
↓-38.5%prior 13
Mud, dirt1 (0.2%)
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford, Chevrolet (listed as CHEV and CHEVROLET), and GMC leading in both years, though the total counts for each decreased. A notable shift occurred in the age demographics of people involved in crashes. The number of individuals aged 65 and older involved in collisions dropped from 247 in 2019 to 161 in 2020, reducing their share of total persons from 14.4% to 11.5%.

Top Vehicle Makes (989 vehicles)

1
FORD178 (18%)
↓-5.8%prior 189
2
CHEV132 (13.3%)
↓-23.3%prior 172
3
CHEVROLET103 (10.4%)
↓-8.8%prior 113
4
GMC46 (4.7%)
↓-24.6%prior 61
5
JEEP32 (3.2%)
↓-13.5%prior 37
6
DODG31 (3.1%)
↓-32.6%prior 46
7
TOYO28 (2.8%)
↑75.0%prior 16
8
BUIC26 (2.6%)
↓-7.1%prior 28
9
TOYT24 (2.4%)
↓-46.7%prior 45
10
TOYOTA23 (2.3%)
↓-37.8%prior 37

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

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

Sex Distribution (870 persons with recorded sex)

Male507 (58.3%)
↓-20.8%prior 640
Female363 (41.7%)
↓-21.1%prior 460

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: 598
  • Total persons involved: 1,398
  • Total vehicles involved: 989

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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