Yearly Traffic Safety Analysis

564 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Marion County, total vehicle crashes increased from 521 in 2021 to 564 in 2022, an 8.3% rise. While overall crashes and injuries saw a slight increase, the most significant year-over-year change was a substantial decrease in fatalities, which fell from 11 in the prior period to 2 in the current period. Crashes attributed to failing to yield the right-of-way from a stop sign more than doubled.

564

▲ 8.3%was 521

Total Crash Events

2

▼ -81.8%was 11

Persons Killed

140

▲ 6.1%was 132

Persons Injured

2

▼ -71.4%was 7

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

Trend Summary

Overall traffic crashes in Marion County trended upward in 2022. The total number of crashes increased by 8.3%, from 521 to 564. Despite this increase in collisions, the number of resulting fatalities dropped from 11 to 2, while total injuries rose slightly from 132 to 140.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 11↓-81.8%

3

Pedestrians Injured

Prior: 3→0.0%

2

Cyclists Injured

Prior: 1↑100.0%

135

Motorists Injured

Prior: 128↑5.5%

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 timing of crashes showed a shift between the two periods. The peak day for crashes moved from Wednesday (85 crashes) in 2021 to Thursday (101 crashes) in 2022. Similarly, the peak hour for collisions shifted from 6 p.m. in the prior year (45 crashes) to a tie between 3 p.m. and 5 p.m. in the current year (48 crashes each).

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 severity of outcomes improved significantly year-over-year. The number of fatal crashes decreased from 7 to 2, and the corresponding fatality count dropped from 11 to 2. Crashes resulting in serious injuries increased in count from 11 to 16, representing a shift from 2.1% to 2.8% of all crashes. The proportion of crashes with no injuries remained stable at approximately 79%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.4%
↓-71.4%prior 7
Serious Injury16serious injury crashes2.8%
↑45.5%prior 11
Minor Injury54minor injury crashes9.6%
↑17.4%prior 46
Possible Injury50possible injury crashes8.9%
↑11.1%prior 45
No Injury442no injury crashes78.4%
↑7.3%prior 412

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 with animals remained the top contributing factor in both periods, though the count decreased slightly from 170 in 2021 to 165 in 2022. The most notable change was in crashes related to 'FTYROW: From stop sign,' which more than doubled in count from 17 to 40. Conversely, crashes attributed to 'Followed too close' decreased in count from 41 to 36.

Officer-Reported Primary Contributing Cause

Animal165 (29.3%)↓-2.9%prior 170
Other (explain in narrative): Other44 (7.8%)↑10.0%prior 40
FTYROW: From stop sign40 (7.1%)↑135.3%prior 17
Followed too close36 (6.4%)↓-12.2%prior 41
Lost Control31 (5.5%)↓-3.1%prior 32
Ran off road - left29 (5.1%)↑70.6%prior 17
Operating vehicle in an reckless, erratic, careless, negligent manner20 (3.5%)↓-9.1%prior 22
Driving too fast for conditions17 (3%)↑13.3%prior 15
Ran off road - straight16 (2.8%)↓-11.1%prior 18
Ran Stop Sign15 (2.7%)↓-6.3%prior 16

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 environmental conditions at the time of crashes remained broadly consistent year-over-year. Crashes during daylight hours increased from 260 to 299, and their share of total crashes rose slightly from 49.9% to 53.0%. Similarly, collisions on dry road surfaces increased from 277 to 304, accounting for a stable majority of incidents in both periods.

Weather

Clear328 (78.3%)
↑23.8%prior 265
Cloudy46 (11.0%)
↓-36.1%prior 72
Snow18 (4.3%)
↑38.5%prior 13
Rain14 (3.3%)
↓-17.6%prior 17
Freezing rain/drizzle5 (1.2%)
Severe Winds3 (0.7%)
Blowing Snow2 (0.5%)
Sleet, hail1 (0.2%)
Other (explain in narrative)1 (0.2%)
Fog, smoke, smog1 (0.2%)

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

Lighting

Daylight299 (70.9%)
↑15.0%prior 260
Dark - roadway not lighted68 (16.1%)
↑1.5%prior 67
Dark - roadway lighted28 (6.6%)
↑3.7%prior 27
Dusk14 (3.3%)
↑55.6%prior 9
Dawn11 (2.6%)
→0.0%prior 11
Dark - unknown roadway lighting2 (0.5%)

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

Road Surface

Dry304 (72.2%)
↑9.7%prior 277
Gravel38 (9.0%)
↑18.8%prior 32
Wet35 (8.3%)
↑20.7%prior 29
Snow25 (5.9%)
↑8.7%prior 23
Ice/frost12 (2.9%)
↑9.1%prior 11
Slush5 (1.2%)
Mud, dirt1 (0.2%)
Other (explain in narrative)1 (0.2%)

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 were consistent, with Ford and Chevrolet models being the most common in both 2021 and 2022. An analysis of persons involved in crashes shows a notable increase in the 65+ age group, which grew from 105 individuals in 2021 to 178 in 2022. The 16-20 and 26-34 age groups also saw increases in their involvement, rising from 120 to 151 and 135 to 172, respectively.

Top Vehicle Makes (851 vehicles)

1
FORD169 (19.9%)
↓-0.6%prior 170
2
CHEV133 (15.6%)
↑18.8%prior 112
3
DODG48 (5.6%)
↑45.5%prior 33
4
JEEP38 (4.5%)
↓-9.5%prior 42
5
TOYT35 (4.1%)
↑133.3%prior 15
6
NISS34 (4%)
↑61.9%prior 21
7
CHEVROLET33 (3.9%)
↓-31.3%prior 48
8
GMC31 (3.6%)
↑10.7%prior 28
9
HOND29 (3.4%)
↑26.1%prior 23
10
BUIC28 (3.3%)
↑47.4%prior 19

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

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

Sex Distribution (802 persons with recorded sex)

Male485 (60.5%)
↑34.0%prior 362
Female317 (39.5%)
↑28.3%prior 247

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: 564
  • Total persons involved: 1,176
  • Total vehicles involved: 851

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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