Yearly Traffic Safety Analysis

1,209 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Story County recorded 1,209 total crashes, a 27.3% decrease from the 1,663 crashes reported in 2019. This overall decline was accompanied by a significant reduction in traffic fatalities, which fell from 9 in the prior year to 2 in the current period. The most notable shift in contributing factors was 'Animal' related incidents becoming the top cause in 2020, increasing in count from 168 to 187, while crashes from other top causes like 'Followed too close' and 'Driving too fast for conditions' decreased.

1,209

▼ -27.3%was 1,663

Total Crash Events

2

▼ -77.8%was 9

Persons Killed

310

▼ -33.2%was 464

Persons Injured

2

▼ -77.8%was 9

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Traffic crashes in Story County showed a significant downward trend year-over-year. The total number of crashes fell by 27.3%, from 1,663 in 2019 to 1,209 in 2020. This decline was also reflected in total injuries, which decreased by 33.2% from 464 to 310, and fatalities, which dropped from 9 to 2.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 9↓-77.8%

12

Pedestrians Injured

Prior: 14↓-14.3%

8

Cyclists Injured

Prior: 14↓-42.9%

290

Motorists Injured

Prior: 435↓-33.3%

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, with Friday being the peak day in both 2020 (263 crashes) and 2019 (265 crashes). The 5 p.m. hour was also the peak hour for both periods, although the number of crashes during this hour decreased from 160 in 2019 to 121 in 2020. Overall crash volumes were lower across most days and hours in 2020 compared to 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 severity of crashes decreased in 2020 compared to 2019. The fatal crash rate dropped from 0.54% to 0.17%, with fatal crashes falling from 9 to 2. While the number and proportion of serious injury crashes increased slightly from 22 (1.3%) to 27 (2.2%), the counts for minor and possible injury crashes both saw decreases. Non-injury crashes constituted a larger share of the total, rising from 77.6% in 2019 to 78.1% in 2020.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.2%
↓-77.8%prior 9
Serious Injury27serious injury crashes2.2%
↑22.7%prior 22
Minor Injury88minor injury crashes7.3%
↓-31.8%prior 129
Possible Injury148possible injury crashes12.2%
↓-30.2%prior 212
No Injury944no injury crashes78.1%
↓-26.9%prior 1,291

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 ranking of top contributing factors shifted between the two periods. In 2020, 'Animal' became the leading factor with 187 incidents, an increase in count from 168 in 2019. Conversely, factors that were previously more common saw significant reductions in count; crashes attributed to 'Followed too close' decreased from 241 to 159, and 'Driving too fast for conditions' fell from 219 to 90.

Officer-Reported Primary Contributing Cause

Animal187 (15.5%)↑11.3%prior 168
Followed too close159 (13.2%)↓-34.0%prior 241
Driving too fast for conditions90 (7.4%)↓-58.9%prior 219
FTYROW: Making left turn73 (6%)↓-33.0%prior 109
Other (explain in narrative): Other69 (5.7%)↓-32.4%prior 102
Lost Control51 (4.2%)↑18.6%prior 43
FTYROW: From stop sign48 (4%)↓-42.2%prior 83
Ran off road - left43 (3.6%)↓-48.8%prior 84
Other (explain in narrative): No improper action41 (3.4%)↓-6.8%prior 44
Improper or erratic lane changing38 (3.1%)↓-28.3%prior 53

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 conditions under which crashes occurred shifted slightly year-over-year. The proportion of crashes on non-dry road surfaces decreased from 34.8% of all crashes in 2019 to 23.2% in 2020, with a corresponding increase in the share of crashes on dry roads from 56.3% to 61.9%. The proportion of crashes occurring in daylight decreased from 65.4% to 58.5%, while the share of crashes in dark conditions increased from 22.7% to 24.2%.

Weather

Clear680 (65.2%)
↓-26.2%prior 921
Cloudy173 (16.6%)
↓-43.5%prior 306
Snow82 (7.9%)
↓-28.7%prior 115
Rain54 (5.2%)
↓-47.6%prior 103
Freezing rain/drizzle25 (2.4%)
↓-7.4%prior 27
Blowing Snow16 (1.5%)
↓-40.7%prior 27
Severe Winds5 (0.5%)
↓-37.5%prior 8
Sleet, hail3 (0.3%)
↓-50.0%prior 6
Fog, smoke, smog3 (0.3%)
↓-40.0%prior 5
Other (explain in narrative)1 (0.1%)

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

Lighting

Daylight708 (67.8%)
↓-34.9%prior 1,087
Dark - roadway lighted195 (18.7%)
↓-17.0%prior 235
Dark - roadway not lighted96 (9.2%)
↓-30.9%prior 139
Dusk24 (2.3%)
↓-11.1%prior 27
Dawn20 (1.9%)
↓-37.5%prior 32
Dark - unknown roadway lighting2 (0.2%)

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

Road Surface

Dry748 (71.4%)
↓-20.2%prior 937
Wet117 (11.2%)
↓-40.3%prior 196
Ice/frost76 (7.3%)
↓-52.2%prior 159
Snow72 (6.9%)
↓-63.6%prior 198
Gravel17 (1.6%)
↑88.9%prior 9
Slush15 (1.4%)
↓-42.3%prior 26
Other (explain in narrative)2 (0.2%)
Mud, dirt1 (0.1%)

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 remained Ford, Chevrolet (as CHEV), and Toyota (as TOYT) in both 2020 and 2019, though the total number of vehicles for each make decreased in line with the overall crash reduction. An analysis of persons involved shows that the 16-20 and 21-25 age groups continued to represent the largest shares in both years. The proportion of persons in the 16-20 age group increased slightly from 16.3% in 2019 to 17.4% in 2020, while the 21-25 age group's share decreased from 18.1% to 17.2%.

Top Vehicle Makes (2,087 vehicles)

1
FORD303 (14.5%)
↓-36.6%prior 478
2
CHEV228 (10.9%)
↓-37.0%prior 362
3
TOYT158 (7.6%)
↓-24.8%prior 210
4
CHEVROLET136 (6.5%)
↓-21.8%prior 174
5
HOND115 (5.5%)
↓-14.2%prior 134
6
JEEP95 (4.6%)
↓-19.5%prior 118
7
TOYOTA65 (3.1%)
↓-47.2%prior 123
8
NISS62 (3%)
↓-36.7%prior 98
9
HONDA55 (2.6%)
↓-35.3%prior 85
10
BUIC54 (2.6%)
↑3.8%prior 52

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

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

Sex Distribution (1,913 persons with recorded sex)

Male1,067 (55.8%)
↓-30.8%prior 1,541
Female846 (44.2%)
↓-30.4%prior 1,216

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: 1,209
  • Total persons involved: 2,707
  • Total vehicles involved: 2,087

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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