Yearly Traffic Safety Analysis

241 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Delaware County recorded 241 total crashes, an increase of 9.1% from the 221 crashes reported in 2021. While total collisions rose, the number of fatalities decreased from three to one. A notable year-over-year shift was the 50% reduction in crashes involving driving under the influence, which fell from 16 in 2021 to 8 in 2022.

241

▲ 9.0%was 221

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

68

▲ 19.3%was 57

Persons Injured

1

▼ -50.0%was 2

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 · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Delaware County show a year-over-year increase in total collisions, rising from 221 in 2021 to 241 in 2022, a 9.1% increase. The number of people injured also rose by 19.3%, from 57 to 68. However, fatalities saw a notable decrease, dropping from three in the prior year to one in the current year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 3↓-66.7%

1

Pedestrians Injured

Prior: 3↓-66.7%

2

Cyclists Injured

Prior: 0→%

65

Motorists Injured

Prior: 53↑22.6%

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 shifted between the two periods. In 2022, the peak day for crashes was Friday with 41 incidents, a change from Thursday (36 incidents) in 2021. The peak hour for crashes remained consistent at 5 p.m. in both years, though the volume during this hour increased from 17 to 23 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

While total crashes increased, the overall severity of incidents lessened in 2022 compared to 2021. The number of fatal crashes decreased from two to one, and the fatal crash rate dropped from 0.9% to 0.41%. The proportion of crashes resulting in serious injuries also declined from 3.6% to 2.5%, while crashes coded with 'possible injury' increased from 7.7% to 11.2% of the total.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
↓-50.0%prior 2
Serious Injury6serious injury crashes2.5%
↓-25.0%prior 8
Minor Injury20minor injury crashes8.3%
↓-20.0%prior 25
Possible Injury27possible injury crashes11.2%
↑58.8%prior 17
No Injury187no injury crashes77.6%
↑10.7%prior 169

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

Collisions involving an animal remained the top contributing factor in both periods, with the count increasing from 72 crashes in 2021 to 89 in 2022. 'Lost Control' was the second-most cited factor in both years, though its count decreased from 25 to 18. Crashes attributed to 'Driving too fast for conditions' increased from 9 incidents in 2021 to 15 in 2022, and incidents of 'Followed too close' rose from 11 to 14.

Officer-Reported Primary Contributing Cause

Animal89 (36.9%)↑23.6%prior 72
Lost Control18 (7.5%)↓-28.0%prior 25
Other (explain in narrative): Other17 (7.1%)↑41.7%prior 12
Driving too fast for conditions15 (6.2%)↑66.7%prior 9
Followed too close14 (5.8%)↑27.3%prior 11
Ran off road - straight12 (5%)↑71.4%prior 7
FTYROW: From stop sign12 (5%)↑20.0%prior 10
Operating vehicle in an reckless, erratic, careless, negligent manner6 (2.5%)↓-14.3%prior 7
Ran Stop Sign5 (2.1%)
FTYROW: From driveway4 (1.7%)

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

Road & Environmental Conditions

Crash conditions remained broadly consistent year-over-year, with the majority of incidents in both 2022 and 2021 occurring in clear weather and during daylight hours. The proportion of crashes under these ideal conditions did not shift significantly. There was an increase in the number of crashes occurring in snowy weather, which rose from 8 incidents in 2021 to 14 in 2022.

Weather

Clear117 (70.9%)
↑9.3%prior 107
Cloudy21 (12.7%)
↓-16.0%prior 25
Snow14 (8.5%)
↑75.0%prior 8
Rain5 (3.0%)
↓-28.6%prior 7
Freezing rain/drizzle4 (2.4%)
Blowing Snow2 (1.2%)
Fog, smoke, smog2 (1.2%)

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

Lighting

Daylight111 (67.3%)
↑4.7%prior 106
Dark - roadway not lighted30 (18.2%)
↓-9.1%prior 33
Dark - roadway lighted15 (9.1%)
↑36.4%prior 11
Dawn5 (3.0%)
Dusk3 (1.8%)
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry114 (69.1%)
↓-1.7%prior 116
Snow15 (9.1%)
↑7.1%prior 14
Wet12 (7.3%)
↑20.0%prior 10
Gravel10 (6.1%)
↑25.0%prior 8
Ice/frost8 (4.8%)
↑33.3%prior 6
Slush5 (3.0%)
Other (explain in narrative)1 (0.6%)

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 showed consistency at the top, with Chevrolet and Ford being the most common in both 2022 and 2021. An analysis of persons involved shows an increase in nearly every age category, corresponding with the rise in overall crash volume. Notably, the number of individuals aged 21-25 involved in crashes increased from 28 in 2021 to 49 in 2022.

Top Vehicle Makes (335 vehicles)

1
CHEV57 (17%)
↑9.6%prior 52
2
FORD56 (16.7%)
↓-5.1%prior 59
3
DODG21 (6.3%)
↑61.5%prior 13
4
CHEVROLET20 (6%)
↓-16.7%prior 24
5
GMC15 (4.5%)
↑7.1%prior 14
6
KIA14 (4.2%)
7
JEEP12 (3.6%)
↓-14.3%prior 14
8
TOYT10 (3%)
9
HOND9 (2.7%)
10
BUIC9 (2.7%)
→0.0%prior 9

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

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

Sex Distribution (305 persons with recorded sex)

Male189 (62.0%)
↑28.6%prior 147
Female116 (38.0%)
↑26.1%prior 92

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: 241
  • Total persons involved: 502
  • Total vehicles involved: 335

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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