Yearly Traffic Safety Analysis

714 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Marshall County, total traffic crashes decreased by 2.2% from 730 in 2021 to 714 in 2022. This overall decline was accompanied by a 13.2% drop in injuries, from 242 to 210, and a 50% reduction in fatalities, from 8 to 4. The most significant year-over-year change was the number of fatal crashes, which fell from 8 in the prior period to just 1 in the current period.

714

▼ -2.2%was 730

Total Crash Events

4

▼ -50.0%was 8

Persons Killed

210

▼ -13.2%was 242

Persons Injured

1

▼ -87.5%was 8

Fatal Crash Events

Note: "Persons Killed" (4) 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

Traffic safety trends in Marshall County showed a slight improvement year-over-year. Total crashes fell from 730 to 714, a decrease of 16 incidents. This downward trend was also reflected in crash outcomes, with total injuries declining from 242 to 210 and fatalities being cut in half from 8 to 4.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 8↓-50.0%

5

Pedestrians Injured

Prior: 5→0.0%

7

Cyclists Injured

Prior: 3↑133.3%

198

Motorists Injured

Prior: 233↓-15.0%

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 timing of crashes shifted between the two periods. In 2022, the peak day for crashes was Thursday with 118 incidents, a change from Friday (136 crashes) in 2021. Similarly, the peak hour moved from 3 p.m. in 2021 (76 crashes) to 4 p.m. in 2022 (55 crashes), indicating a slight shift in the afternoon rush hour collision pattern.

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 saw a notable shift year-over-year. Fatal crashes dropped from 8 incidents (1.1% of total) in 2021 to 1 incident (0.1% of total) in 2022. However, the count of serious injury crashes increased from 15 to 22, representing a rise from 2.1% to 3.1% of all crashes. Crashes resulting in minor or possible injuries decreased, while non-injury crashes increased from 530 to 550, making up a larger share of the total at 77.0% in 2022 compared to 72.6% in 2021.

Severity is per crash event (most severe injury). 1 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.1%
↓-87.5%prior 8
Serious Injury22serious injury crashes3.1%
↑46.7%prior 15
Minor Injury64minor injury crashes9%
↓-19.0%prior 79
Possible Injury77possible injury crashes10.8%
↓-21.4%prior 98
No Injury550no injury crashes77%
↑3.8%prior 530

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 primary contributing factors to crashes remained consistent, though their counts shifted. Collisions involving an animal were the leading factor in both years, with the count increasing by 29% from 120 in 2021 to 155 in 2022. 'Lost Control' remained the second-most common factor but saw its count decrease from 68 to 62. 'Failure to Yield Right of Way from a stop sign' was the third-leading factor in both periods, with a nearly identical count of 53 in 2021 and 52 in 2022.

Officer-Reported Primary Contributing Cause

Animal155 (21.7%)↑29.2%prior 120
Lost Control62 (8.7%)↓-8.8%prior 68
FTYROW: From stop sign52 (7.3%)↓-1.9%prior 53
Followed too close39 (5.5%)↓-9.3%prior 43
Driving too fast for conditions36 (5%)→0.0%prior 36
Other (explain in narrative): Other34 (4.8%)↓-19.0%prior 42
FTYROW: Making left turn33 (4.6%)↑3.1%prior 32
Ran Stop Sign29 (4.1%)↑11.5%prior 26
Ran off road - left26 (3.6%)↓-7.1%prior 28
Ran off road - straight25 (3.5%)↑4.2%prior 24

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

Road & Environmental Conditions

While most crashes in both years occurred in clear weather on dry roads, there was a shift in the proportion of crashes under adverse conditions. Crashes during snowy conditions increased from 17 in 2021 to 31 in 2022, and incidents in the rain more than doubled from 12 to 26. Correspondingly, crashes on snowy road surfaces increased from 33 to 48, and crashes on wet surfaces rose from 36 to 50. Crashes in darkness without roadway lighting also saw an increase, from 101 to 112 incidents.

Weather

Clear411 (70.4%)
↓-10.3%prior 458
Cloudy81 (13.9%)
↓-13.8%prior 94
Snow31 (5.3%)
↑82.4%prior 17
Rain26 (4.5%)
↑116.7%prior 12
Blowing Snow15 (2.6%)
↑114.3%prior 7
Freezing rain/drizzle10 (1.7%)
↓-56.5%prior 23
Severe Winds5 (0.9%)
↓-54.5%prior 11
Fog, smoke, smog4 (0.7%)
↓-20.0%prior 5
Other (explain in narrative)1 (0.2%)

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

Lighting

Daylight369 (62.8%)
↓-9.6%prior 408
Dark - roadway not lighted112 (19.0%)
↑10.9%prior 101
Dark - roadway lighted89 (15.1%)
↓-11.0%prior 100
Dusk9 (1.5%)
→0.0%prior 9
Dawn8 (1.4%)
↓-20.0%prior 10
Dark - unknown roadway lighting1 (0.2%)

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

Road Surface

Dry424 (72.2%)
↓-12.6%prior 485
Wet50 (8.5%)
↑38.9%prior 36
Snow48 (8.2%)
↑45.5%prior 33
Ice/frost39 (6.6%)
↓-25.0%prior 52
Gravel19 (3.2%)
↑46.2%prior 13
Slush4 (0.7%)
↓-55.6%prior 9
Other (explain in narrative)2 (0.3%)
Mud, dirt1 (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 remained relatively stable, with Chevrolet and Ford being the most common in both 2021 and 2022. A notable demographic shift occurred in the age of persons involved in collisions. The number of individuals in the 21-25 age group increased by 36%, from 114 in 2021 to 155 in 2022. The 26-34 age group also saw an increase from 218 to 245 persons involved, while most other age groups remained stable or saw slight decreases.

Top Vehicle Makes (1,129 vehicles)

1
CHEV212 (18.8%)
↑37.7%prior 154
2
FORD171 (15.1%)
↓-2.3%prior 175
3
HOND65 (5.8%)
↑27.5%prior 51
4
TOYT57 (5%)
↑29.5%prior 44
5
DODG52 (4.6%)
↑8.3%prior 48
6
CHEVROLET51 (4.5%)
↓-40.0%prior 85
7
JEEP50 (4.4%)
↑19.0%prior 42
8
GMC39 (3.5%)
↓-17.0%prior 47
9
NISS31 (2.7%)
↑10.7%prior 28
10
CHRY30 (2.7%)
↑36.4%prior 22

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

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

Sex Distribution (1,020 persons with recorded sex)

Male594 (58.2%)
↑8.0%prior 550
Female426 (41.8%)
↑14.8%prior 371

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: 714
  • Total persons involved: 1,561
  • Total vehicles involved: 1,129

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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