Yearly Traffic Safety Analysis

1,313 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Dallas County, total traffic crashes increased by 3.6% from 1,267 in 2021 to 1,313 in 2022. While overall crash volume saw a modest rise, the most significant year-over-year change was a 66.7% increase in fatalities, which grew from 6 to 10. The number of injuries also rose slightly from 403 to 418.

1,313

▲ 3.6%was 1,267

Total Crash Events

10

▲ 66.7%was 6

Persons Killed

418

▲ 3.7%was 403

Persons Injured

10

▲ 66.7%was 6

Fatal Crash Events

Note: "Persons Killed" (10) counts individual fatalities across all crash events. "Fatal" in the severity table below (10) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data from Dallas County indicates a rising trend in both the frequency and severity of collisions year-over-year. Total crashes increased by 3.6%, from 1,267 in 2021 to 1,313 in 2022. This was accompanied by a 3.7% increase in injuries and a more substantial 66.7% increase in fatalities.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

1

Cyclists Killed

Prior: 0→%

9

Motorists Killed

Prior: 5↑80.0%

5

Pedestrians Injured

Prior: 5→0.0%

6

Cyclists Injured

Prior: 8↓-25.0%

407

Motorists Injured

Prior: 389↑4.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · 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 between the two periods. Friday was the peak day for crashes in both 2022 (231 crashes) and 2021 (233 crashes). However, the peak hour for collisions shifted slightly earlier, from the 5 p.m. hour in 2021 (134 crashes) to a tie between the 3 p.m. and 4 p.m. hours in 2022, each with 124 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes worsened year-over-year, with the number of fatal crashes increasing from 6 in 2021 to 10 in 2022, and the fatal crash rate rising from 0.47 to 0.76 per 100 crashes. The overall proportion of crashes resulting in any injury remained stable at approximately 25%. Within injury crashes, the share of minor injury collisions increased from 8.2% to 10.3% of all crashes, while the share of possible injury crashes decreased from 15.9% to 13.6%.

Outcome by Severity (Crash Events)

Fatal10fatal crashes0.8%
↑66.7%prior 6
Serious Injury18serious injury crashes1.4%
↓-5.3%prior 19
Minor Injury135minor injury crashes10.3%
↑29.8%prior 104
Possible Injury179possible injury crashes13.6%
↓-10.9%prior 201
No Injury971no injury crashes74%
↑3.6%prior 937

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes were consistent across both years, though their order shifted. In 2022, collisions involving an animal became the top factor with 194 incidents, an 8% increase in count from 180 in 2021 when it was ranked second. Conversely, 'Followed too close,' the top factor in 2021 with 202 crashes, saw its count decrease by 13% to 175 crashes in 2022, dropping it to the second position. 'Driving too fast for conditions' remained the third-leading factor, with its incident count rising from 86 to 94.

Officer-Reported Primary Contributing Cause

Animal194 (14.8%)↑7.8%prior 180
Followed too close175 (13.3%)↓-13.4%prior 202
Driving too fast for conditions94 (7.2%)↑9.3%prior 86
FTYROW: From stop sign86 (6.5%)↑30.3%prior 66
Other (explain in narrative): Other77 (5.9%)↑10.0%prior 70
FTYROW: Making left turn74 (5.6%)↑51.0%prior 49
Ran off road - left63 (4.8%)↑16.7%prior 54
Lost Control47 (3.6%)→0.0%prior 47
Improper or erratic lane changing44 (3.4%)↑266.7%prior 12
Operating vehicle in an reckless, erratic, careless, negligent manner39 (3%)↑30.0%prior 30

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across different environmental conditions showed little change year-over-year. In both 2022 and 2021, the vast majority of collisions occurred in clear weather (62.8% and 64.2%, respectively) and on dry road surfaces (67.4% and 67.9%, respectively). Similarly, the proportion of crashes happening during daylight hours remained stable, accounting for 64.8% of incidents in 2022 compared to 61.8% in 2021. There were no significant shifts indicating that adverse weather, lighting, or road surface conditions played a substantially different role between the two periods.

Weather

Clear825 (71.7%)
↑1.4%prior 814
Cloudy175 (15.2%)
↑17.4%prior 149
Snow65 (5.6%)
↑38.3%prior 47
Rain41 (3.6%)
→0.0%prior 41
Blowing Snow16 (1.4%)
↑33.3%prior 12
Freezing rain/drizzle11 (1.0%)
↓-52.2%prior 23
Other (explain in narrative)7 (0.6%)
↓-30.0%prior 10
Severe Winds7 (0.6%)
Fog, smoke, smog2 (0.2%)
↓-60.0%prior 5
Sleet, hail2 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight851 (74.1%)
↑8.7%prior 783
Dark - roadway lighted137 (11.9%)
↓-8.1%prior 149
Dark - roadway not lighted107 (9.3%)
↑3.9%prior 103
Dusk35 (3.0%)
↓-12.5%prior 40
Dawn16 (1.4%)
↓-44.8%prior 29
Dark - unknown roadway lighting3 (0.3%)
↓-66.7%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry885 (76.9%)
↑2.9%prior 860
Wet103 (8.9%)
↑5.1%prior 98
Snow70 (6.1%)
↑11.1%prior 63
Ice/frost66 (5.7%)
↑4.8%prior 63
Gravel20 (1.7%)
↑33.3%prior 15
Other (explain in narrative)4 (0.3%)
Slush3 (0.3%)
↓-66.7%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes and the age distribution of persons involved were largely consistent year-over-year. Chevrolet and Ford were the top two vehicle makes involved in collisions in both periods. The age demographics of individuals in crashes also showed stability; for instance, the 16-20 age group represented 15.4% of all persons in both 2022 and 2021. The 26-44 age bracket continued to be the largest group, accounting for a combined 33.4% of persons in 2022, similar to its 32.7% share in 2021.

Top Vehicle Makes (2,309 vehicles)

1
CHEV338 (14.6%)
↑28.0%prior 264
2
FORD320 (13.9%)
↑2.2%prior 313
3
TOYT129 (5.6%)
↑14.2%prior 113
4
HOND119 (5.2%)
↑46.9%prior 81
5
JEEP109 (4.7%)
↑19.8%prior 91
6
CHEVROLET109 (4.7%)
↓-26.8%prior 149
7
NISS103 (4.5%)
↑33.8%prior 77
8
DODG76 (3.3%)
↑26.7%prior 60
9
HYUN75 (3.2%)
↑74.4%prior 43
10
GMC68 (2.9%)
↓-1.4%prior 69

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

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

Sex Distribution (2,226 persons with recorded sex)

Male1,215 (54.6%)
↑13.7%prior 1,069
Female1,011 (45.4%)
↑24.4%prior 813

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 1,313
  • Total persons involved: 2,960
  • Total vehicles involved: 2,309

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: 2022." Published September 9, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2022-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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