Yearly Traffic Safety Analysis

114 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Monroe County recorded 114 vehicle crashes, a 2.7% increase from the 111 crashes reported in 2021. Total injuries remained unchanged at 37 for both years. The most significant year-over-year change was the occurrence of three fatal crashes resulting in three fatalities in 2022, whereas no fatal crashes were recorded in the prior year.

114

2.7%was 111

Total Crash Events

3

Persons Killed

37

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 Monroe County show a slight increase, with total collisions rising by 2.7% from 111 in 2021 to 114 in 2022. While the number of injuries remained constant at 37, the number of fatalities increased from zero in 2021 to three in 2022, marking a negative shift in crash outcomes.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0%

0

Cyclists Killed

Prior: 00.0%

2

Motorists Killed

Prior: 0%

2

Pedestrians Injured

Prior: 1100.0%

1

Cyclists Injured

Prior: 0%

34

Motorists Injured

Prior: 36-5.6%

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 23 incidents, a change from 2021 when Monday was the peak day with 24 crashes. The peak hour also shifted earlier, from 5 p.m. in 2021 (15 crashes) to 3 p.m. in 2022 (15 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 worsened in 2022 with the recording of three fatal crashes, which accounted for 2.6% of all incidents, compared to zero fatal crashes in 2021. The proportion of serious injury crashes decreased from 2.7% in 2021 to 0.9% in 2022. Conversely, the share of minor injury crashes rose from 12.6% in 2021 to 15.8% in 2022.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2.6%
Serious Injury1serious injury crashes0.9%
-66.7%prior 3
Minor Injury18minor injury crashes15.8%
28.6%prior 14
Possible Injury12possible injury crashes10.5%
-7.7%prior 13
No Injury80no injury crashes70.2%
-1.2%prior 81

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 years, though the count of these incidents decreased from 43 in 2021 to 39 in 2022. The number of crashes attributed to 'Followed too close' was halved, dropping from a count of 10 to 5 year-over-year. In contrast, crashes involving 'Failure to Yield Right of Way from a stop sign' increased in count from 3 in 2021 to 5 in 2022.

Officer-Reported Primary Contributing Cause

Animal39 (34.2%)-9.3%prior 43
Other (explain in narrative): Other9 (7.9%)80.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner5 (4.4%)
Lost Control5 (4.4%)-37.5%prior 8
Followed too close5 (4.4%)-50.0%prior 10
FTYROW: From stop sign5 (4.4%)
Driving too fast for conditions5 (4.4%)
Ran off road - straight5 (4.4%)
Ran Stop Sign3 (2.6%)
FTYROW: Making left turn3 (2.6%)

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 largely consistent year-over-year, with the majority of incidents in both 2021 and 2022 occurring during daylight (53 crashes each year) and on dry roads (62 and 61 crashes, respectively). There was a slight increase in crashes on snow-covered roads, rising from 2 incidents in 2021 to 4 in 2022. Crashes in dark, unlighted conditions also saw a small increase from 13 to 16.

Weather

Clear64 (73.6%)
1.6%prior 63
Cloudy10 (11.5%)
-16.7%prior 12
Rain5 (5.7%)
0.0%prior 5
Snow4 (4.6%)
Freezing rain/drizzle2 (2.3%)
Severe Winds1 (1.1%)
Blowing Snow1 (1.1%)

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

Lighting

Daylight53 (60.9%)
0.0%prior 53
Dark - roadway not lighted16 (18.4%)
23.1%prior 13
Dawn11 (12.6%)
22.2%prior 9
Dark - roadway lighted6 (6.9%)
Dark - unknown roadway lighting1 (1.1%)

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

Road Surface

Dry61 (70.1%)
-1.6%prior 62
Wet10 (11.5%)
11.1%prior 9
Gravel6 (6.9%)
Ice/frost4 (4.6%)
Snow4 (4.6%)
Mud, dirt1 (1.1%)
Slush1 (1.1%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved makes in both years; the count of Fords increased from 32 to 39, while combined Chevrolet models decreased from 43 to 39. Regarding the age of persons involved, the 35-44 age group remained the most represented, increasing from 46 to 51 individuals. The number of persons in the 55-64 age group involved in crashes grew from 30 to 42, while involvement for the 65+ age group decreased from 31 to 17.

Top Vehicle Makes (168 vehicles)

1
FORD39 (23.2%)
21.9%prior 32
2
CHEV27 (16.1%)
-10.0%prior 30
3
CHEVROLET12 (7.1%)
-7.7%prior 13
4
DODGE8 (4.8%)
5
JEEP8 (4.8%)
60.0%prior 5
6
GMC7 (4.2%)
7
DODG6 (3.6%)
-68.4%prior 19
8
CHRY5 (3%)
9
NISS4 (2.4%)
10
TOYOTA4 (2.4%)

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

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

Sex Distribution (158 persons with recorded sex)

Male96 (60.8%)
26.3%prior 76
Female62 (39.2%)
29.2%prior 48

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: 114
  • Total persons involved: 248
  • Total vehicles involved: 168

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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