Yearly Traffic Safety Analysis

1,894 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Pottawattamie County, total traffic crashes decreased by 15.0% from 2,229 in 2019 to 1,894 in 2020. Despite this overall decline in collisions, the number of crashes involving driving under the influence (DUI) increased from 73 to 82.

1,894

▼ -15.0%was 2,229

Total Crash Events

12

▼ -7.7%was 13

Persons Killed

671

▼ -5.5%was 710

Persons Injured

12

Fatal Crash Events

Note: "Persons Killed" (12) counts individual fatalities across all crash events. "Fatal" in the severity table below (12) 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 collisions in Pottawattamie County showed a downward trend year-over-year. The total number of crashes fell by 15.0%, from 2,229 to 1,894. Similarly, total injuries decreased by 5.5% from 710 to 671, while fatalities saw a slight reduction from 13 to 12.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 1↓-100.0%

9

Motorists Killed

Prior: 12↓-25.0%

14

Pedestrians Injured

Prior: 16↓-12.5%

12

Cyclists Injured

Prior: 22↓-45.5%

645

Motorists Injured

Prior: 670↓-3.7%

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 the two periods. Friday was the day with the most crashes in both 2019 (405 crashes) and 2020 (300 crashes), and the peak hour for collisions was 3 PM in both years. While the peak times did not shift, the volume of crashes during these peaks and across all other times was lower 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

While total crashes declined, the severity of crashes that did occur worsened on a proportional basis. The number of fatal crashes was unchanged at 12 for both years, causing the fatal crash rate to rise from 0.54 to 0.63 per 100 crashes. Furthermore, the count of serious injury crashes increased from 46 in 2019 to 54 in 2020, and their share of all crashes grew from 2.1% to 2.9%.

Outcome by Severity (Crash Events)

Fatal12fatal crashes0.6%
→0.0%prior 12
Serious Injury54serious injury crashes2.9%
↑17.4%prior 46
Minor Injury158minor injury crashes8.3%
↓-16.0%prior 188
Possible Injury391possible injury crashes20.6%
↓-3.0%prior 403
No Injury1,279no injury crashes67.5%
↓-19.1%prior 1,580

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 for crashes were consistent across both years, with 'Followed too close,' 'Animal,' and 'Ran off road - left' ranking as the top three. The count for the top factor, 'Followed too close,' decreased by 35.5% from 335 incidents in 2019 to 216 in 2020. In contrast, crashes attributed to a driver 'Operating vehicle in a reckless, erratic, careless, negligent manner' saw a 73.3% increase in count, rising from 30 to 52 incidents.

Officer-Reported Primary Contributing Cause

Followed too close216 (11.4%)↓-35.5%prior 335
Animal157 (8.3%)↓-19.5%prior 195
Ran off road - left148 (7.8%)↓-11.4%prior 167
Lost Control129 (6.8%)↓-21.8%prior 165
Ran off road - straight98 (5.2%)↑14.0%prior 86
Ran Stop Sign97 (5.1%)↑24.4%prior 78
Ran Traffic Signal81 (4.3%)↑3.8%prior 78
FTYROW: From stop sign77 (4.1%)↓-7.2%prior 83
Driving too fast for conditions74 (3.9%)↓-10.8%prior 83
Other (explain in narrative): Other71 (3.7%)↓-39.8%prior 118

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 in adverse conditions decreased from 2019 to 2020. Collisions on wet, snowy, or icy roads declined from 526 to 367, and their share of total crashes fell from 23.6% to 19.4%. Consequently, the share of crashes occurring on dry road surfaces increased from 67.8% in 2019 to 72.3% in 2020.

Weather

Clear1,255 (71.8%)
↓-11.4%prior 1,417
Cloudy264 (15.1%)
↓-22.8%prior 342
Rain80 (4.6%)
↓-19.2%prior 99
Snow78 (4.5%)
↓-33.9%prior 118
Freezing rain/drizzle34 (1.9%)
↓-33.3%prior 51
Blowing Snow18 (1.0%)
↑5.9%prior 17
Fog, smoke, smog9 (0.5%)
↓-18.2%prior 11
Severe Winds5 (0.3%)
Other (explain in narrative)3 (0.2%)
Sleet, hail2 (0.1%)

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

Lighting

Daylight1,171 (66.6%)
↓-16.8%prior 1,407
Dark - roadway lighted308 (17.5%)
↓-18.7%prior 379
Dark - roadway not lighted185 (10.5%)
↓-2.1%prior 189
Dusk52 (3.0%)
↓-7.1%prior 56
Dawn34 (1.9%)
↑6.3%prior 32
Dark - unknown roadway lighting8 (0.5%)
↑33.3%prior 6

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

Road Surface

Dry1,370 (78.0%)
↓-9.4%prior 1,512
Wet196 (11.2%)
↓-26.3%prior 266
Snow80 (4.6%)
↓-35.5%prior 124
Ice/frost73 (4.2%)
↓-37.1%prior 116
Slush18 (1.0%)
↓-10.0%prior 20
Gravel15 (0.9%)
→0.0%prior 15
Mud, dirt2 (0.1%)
Other (explain in narrative)1 (0.1%)
↓-80.0%prior 5
Oil1 (0.1%)

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 remained consistent, with Chevrolet and Ford vehicles being the most numerous in both 2019 and 2020, although the total counts for both makes decreased. The demographic profile of persons involved in crashes also showed little change year-over-year. The proportional representation of most age groups, such as the 16-20 and 26-34 age brackets, remained stable between the two periods.

Top Vehicle Makes (3,234 vehicles)

1
FORD481 (14.9%)
↓-17.9%prior 586
2
CHEVROLET298 (9.2%)
↓-1.7%prior 303
3
CHEV284 (8.8%)
↓-24.7%prior 377
4
NR144 (4.5%)
↓-33.3%prior 216
5
KIA124 (3.8%)
↑7.8%prior 115
6
JEEP115 (3.6%)
↓-16.1%prior 137
7
DODG114 (3.5%)
↓-10.2%prior 127
8
DODGE107 (3.3%)
↓-14.4%prior 125
9
NISSAN97 (3%)
↑3.2%prior 94
10
TOYOTA94 (2.9%)
↓-18.3%prior 115

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

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

Sex Distribution (2,727 persons with recorded sex)

Male1,644 (60.3%)
↓-11.9%prior 1,866
Female1,083 (39.7%)
↓-20.0%prior 1,353

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: 1,894
  • Total persons involved: 4,406
  • Total vehicles involved: 3,234

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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