Yearly Traffic Safety Analysis

278 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Tama County, there were 278 total crashes in 2020, a 1.4% decrease from the 282 crashes recorded in 2019. While total fatalities remained unchanged at one, the number of injuries fell by 12.5%. The most notable year-over-year shift occurred in the geographic distribution of crashes, with Toledo surpassing Tama as the municipality with the highest number of incidents.

278

▼ -1.4%was 282

Total Crash Events

1

Persons Killed

84

▼ -12.5%was 96

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 crash trends in Tama County showed a slight decline from 2019 to 2020. Total crashes decreased by 1.4%, from 282 to 278. While the number of fatalities was stable at one for both years, total injuries saw a more significant reduction, falling 12.5% from 96 in 2019 to 84 in 2020.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

84

Motorists Injured

Prior: 95↓-11.6%

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 shifted between the two periods. In 2020, Wednesday became the peak day for crashes with 46 incidents, a change from 2019 when Friday and Sunday shared the highest frequency with 45 crashes each. The peak hour for collisions also shifted slightly earlier, from 9 p.m. in 2019 (24 crashes) to a tie between 5 p.m. and 8 p.m. in 2020 (22 crashes each).

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 was broadly similar year-over-year, with one fatal crash recorded in both 2019 and 2020. However, the proportion of crashes resulting in any injury decreased from 26.5% in 2019 to 23.8% in 2020, driven by a drop in the share of both serious and minor injury crashes. Consequently, the share of crashes with no reported injuries increased from 73.0% to 75.9%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
→0.0%prior 1
Serious Injury6serious injury crashes2.2%
↓-40.0%prior 10
Minor Injury22minor injury crashes7.9%
↓-26.7%prior 30
Possible Injury38possible injury crashes13.7%
↑8.6%prior 35
No Injury211no injury crashes75.9%
↑2.4%prior 206

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 leading contributing factor in both years, with the count increasing from 107 in 2019 to 112 in 2020. "Lost Control" also remained the second-most cited factor, with its incident count rising from 21 to 24. A notable change was the factor "Driving too fast for conditions," which dropped from 15 incidents in 2019 to 10 in 2020, a 33.3% decrease in count.

Officer-Reported Primary Contributing Cause

Animal112 (40.3%)↑4.7%prior 107
Lost Control24 (8.6%)↑14.3%prior 21
Ran off road - straight18 (6.5%)↑28.6%prior 14
Ran off road - left14 (5%)↑27.3%prior 11
FTYROW: From stop sign14 (5%)↑16.7%prior 12
Other (explain in narrative): Other13 (4.7%)→0.0%prior 13
Driving too fast for conditions10 (3.6%)↓-33.3%prior 15
Ran Stop Sign8 (2.9%)↑60.0%prior 5
Swerving/Evasive Action5 (1.8%)
Operating vehicle in an reckless, erratic, careless, negligent manner5 (1.8%)↓-50.0%prior 10

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

Road & Environmental Conditions

Year-over-year data indicates a shift in the conditions under which crashes occurred. The proportion of collisions happening in daylight decreased from 45.0% in 2019 to 36.7% in 2020. Similarly, the share of crashes on dry road surfaces fell from 53.9% to 45.0%, and incidents in clear weather dropped from 50.7% to 41.4% of the total.

Weather

Clear115 (63.2%)
↓-19.6%prior 143
Cloudy38 (20.9%)
↓-9.5%prior 42
Snow11 (6.0%)
↑57.1%prior 7
Rain6 (3.3%)
↓-40.0%prior 10
Freezing rain/drizzle4 (2.2%)
Fog, smoke, smog4 (2.2%)
Severe Winds3 (1.6%)
Sleet, hail1 (0.5%)

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

Lighting

Daylight102 (55.7%)
↓-19.7%prior 127
Dark - roadway not lighted50 (27.3%)
↓-19.4%prior 62
Dark - roadway lighted16 (8.7%)
↑14.3%prior 14
Dusk7 (3.8%)
↓-22.2%prior 9
Dawn6 (3.3%)
Dark - unknown roadway lighting2 (1.1%)

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

Road Surface

Dry125 (68.7%)
↓-17.8%prior 152
Wet19 (10.4%)
→0.0%prior 19
Snow15 (8.2%)
↓-16.7%prior 18
Ice/frost11 (6.0%)
↓-8.3%prior 12
Gravel8 (4.4%)
↓-38.5%prior 13
Other (explain in narrative)2 (1.1%)
Slush2 (1.1%)

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

Vehicles & Demographics

The top three vehicle makes involved in crashes—Chevrolet, Ford, and Dodge—remained consistent in their rankings between 2019 and 2020 with similar involvement counts. However, the age demographics of persons involved in crashes shifted, with a notable increase in the 16-20 age group (from 75 to 92 individuals) and the 26-34 age group (from 71 to 96 individuals). Conversely, the 45-54 age group saw a decrease in involvement from 92 to 77 persons.

Top Vehicle Makes (369 vehicles)

1
CHEV62 (16.8%)
↓-8.8%prior 68
2
FORD60 (16.3%)
↓-4.8%prior 63
3
CHEVROLET25 (6.8%)
↑47.1%prior 17
4
DODG20 (5.4%)
↓-23.1%prior 26
5
DODGE16 (4.3%)
↑60.0%prior 10
6
JEEP15 (4.1%)
↑15.4%prior 13
7
TOYT14 (3.8%)
↑7.7%prior 13
8
HOND14 (3.8%)
→0.0%prior 14
9
CHRY12 (3.3%)
→0.0%prior 12
10
BUIC9 (2.4%)
↓-10.0%prior 10

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

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

Sex Distribution (349 persons with recorded sex)

Male223 (63.9%)
↑5.7%prior 211
Female126 (36.1%)
↓-13.1%prior 145

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: 278
  • Total persons involved: 567
  • Total vehicles involved: 369

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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Tama County, IA Crash Report — 2020 | ThatCarHitMe.com