Yearly Traffic Safety Analysis

822 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Des Moines County, 822 traffic crashes were recorded in 2022, a figure nearly identical to the 819 crashes in 2021. While overall crash volume remained stable, the number of resulting injuries decreased by 7.8%, from 206 to 190. Conversely, the number of fatalities increased from 3 in the prior year to 4 in the current year.

822

▲ 0.4%was 819

Total Crash Events

4

▲ 33.3%was 3

Persons Killed

190

▼ -7.8%was 206

Persons Injured

4

▲ 33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

Overall crash trends in Des Moines County were largely stable year-over-year, with total crashes increasing by a marginal 0.4% from 819 in 2021 to 822 in 2022. However, the outcomes of these incidents shifted, as total reported injuries decreased by 7.8% while total fatalities increased from 3 to 4.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 1→0.0%

0

Cyclists Killed

Prior: 1↓-100.0%

3

Motorists Killed

Prior: 1↑200.0%

0

Other Killed

Prior: 0→0.0%

4

Pedestrians Injured

Prior: 5↓-20.0%

4

Cyclists Injured

Prior: 7↓-42.9%

180

Motorists Injured

Prior: 194↓-7.2%

2

Other Injured

Prior: 0→%

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 showed some consistency and some change year-over-year. Friday remained the day with the most crashes in both 2021 (142 crashes) and 2022 (138 crashes). However, the peak hour for collisions shifted from 1 p.m. in 2021 (62 crashes) to 3 p.m. in 2022 (73 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

Crash severity outcomes varied between the two periods. The number of fatal crashes increased from 3 in 2021 to 4 in 2022, while serious injury crashes decreased from 17 to 10. Crashes resulting in possible injury increased from 101 to 129. No-injury crashes remained the most common outcome, accounting for 77.4% of incidents in 2021 and 75.8% in 2022.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.5%
↑33.3%prior 3
Serious Injury10serious injury crashes1.2%
↓-41.2%prior 17
Minor Injury56minor injury crashes6.8%
↓-12.5%prior 64
Possible Injury129possible injury crashes15.7%
↑27.7%prior 101
No Injury623no injury crashes75.8%
↓-1.7%prior 634

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, with 'Animal' being the most cited cause in both 2021 (123 crashes) and 2022 (143 crashes), a 16.3% increase in count. Crashes attributed to 'Followed too close' also rose in count from 56 to 61. In contrast, crashes involving 'Failure to yield right of way when making a left turn' saw a significant drop in count, from 48 incidents in 2021 to 27 in 2022.

Officer-Reported Primary Contributing Cause

Animal143 (17.4%)↑16.3%prior 123
Other (explain in narrative): Other88 (10.7%)↑4.8%prior 84
Followed too close61 (7.4%)↑8.9%prior 56
Driver Distraction: Other interior distraction54 (6.6%)↑20.0%prior 45
Ran off road - left44 (5.4%)↑7.3%prior 41
FTYROW: From stop sign40 (4.9%)↑37.9%prior 29
Lost Control35 (4.3%)↑6.1%prior 33
Driving too fast for conditions29 (3.5%)↓-31.0%prior 42
FTYROW: Making left turn27 (3.3%)↓-43.8%prior 48
Ran Stop Sign26 (3.2%)↑52.9%prior 17

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

Road & Environmental Conditions

In both 2021 and 2022, the majority of crashes occurred in clear weather and on dry roads. Crashes in clear weather accounted for 58.1% of the total in 2021 and 60.5% in 2022. Incidents on wet roads decreased from 96 to 78 year-over-year. Conversely, the number of crashes occurring on roads with snow or ice increased from 73 in 2021 to 82 in 2022.

Weather

Clear497 (72.6%)
↑4.4%prior 476
Cloudy83 (12.1%)
↓-35.2%prior 128
Rain44 (6.4%)
↓-22.8%prior 57
Snow40 (5.8%)
↑21.2%prior 33
Freezing rain/drizzle14 (2.0%)
↑100.0%prior 7
Other (explain in narrative)3 (0.4%)
Blowing Snow2 (0.3%)
Severe Winds1 (0.1%)
Fog, smoke, smog1 (0.1%)

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

Lighting

Daylight477 (69.1%)
↓-4.4%prior 499
Dark - roadway lighted131 (19.0%)
↑7.4%prior 122
Dark - roadway not lighted46 (6.7%)
↓-9.8%prior 51
Dusk24 (3.5%)
↓-4.0%prior 25
Dark - unknown roadway lighting7 (1.0%)
Dawn5 (0.7%)
↓-64.3%prior 14

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

Road Surface

Dry501 (72.7%)
↓-4.2%prior 523
Wet78 (11.3%)
↓-18.8%prior 96
Snow53 (7.7%)
↑3.9%prior 51
Ice/frost29 (4.2%)
↑31.8%prior 22
Gravel16 (2.3%)
↑45.5%prior 11
Slush8 (1.2%)
↑14.3%prior 7
Sand2 (0.3%)
Other (explain in narrative)2 (0.3%)

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

Vehicles & Demographics

Ford and Chevrolet were the top two vehicle makes involved in crashes in both years; the count for Ford vehicles increased from 220 in 2021 to 234 in 2022. An analysis of persons involved in crashes shows an increase in the 21-25 age group, which grew from 134 individuals in 2021 to 171 in 2022. The 65+ age group also saw its involvement increase from 193 individuals to 231.

Top Vehicle Makes (1,394 vehicles)

1
FORD234 (16.8%)
↑6.4%prior 220
2
CHEV158 (11.3%)
↑12.1%prior 141
3
DODG84 (6%)
↑35.5%prior 62
4
CHEVROLET77 (5.5%)
↓-38.9%prior 126
5
KIA76 (5.5%)
↑22.6%prior 62
6
TOYO60 (4.3%)
↑33.3%prior 45
7
GMC59 (4.2%)
→0.0%prior 59
8
NR58 (4.2%)
↑38.1%prior 42
9
JEEP56 (4%)
↑19.1%prior 47
10
HOND38 (2.7%)
↑5.6%prior 36

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

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

Sex Distribution (1,151 persons with recorded sex)

Male608 (52.8%)
↑1.2%prior 601
Female543 (47.2%)
↑10.4%prior 492

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: 822
  • Total persons involved: 1,894
  • Total vehicles involved: 1,394

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

ThatCarHitMe.com · An Injuria.ai Company