Yearly Traffic Safety Analysis

121 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Montgomery County, total traffic crashes increased by 4.3% from 116 in 2021 to 121 in 2022. While total injuries saw a decrease, the most significant year-over-year change was the increase in fatalities, which rose from zero in the prior period to four in the current period.

121

▲ 4.3%was 116

Total Crash Events

4

Persons Killed

25

▼ -16.7%was 30

Persons Injured

4

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 volume in Montgomery County saw a slight upward trend, increasing from 116 incidents in 2021 to 121 in 2022. This rise in total crashes was accompanied by a more severe shift in outcomes, with fatalities increasing from 0 to 4, even as the total number of people injured decreased from 30 to 25.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 0→%

0

Pedestrians Injured

Prior: 0→0.0%

1

Cyclists Injured

Prior: 0→%

24

Motorists Injured

Prior: 30↓-20.0%

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 remained broadly consistent year-over-year. Friday was the peak day for crashes in both 2022 (27 crashes) and 2021 (21 crashes). The afternoon commute continued to be the period with the highest crash frequency, though the specific peak hour shifted slightly from 5 p.m. in 2021 (12 crashes) to 4 p.m. in 2022 (11 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 significantly in 2022 compared to 2021. There were four fatal crashes resulting in four deaths in 2022, whereas there were no fatal crashes in the prior year. The count of serious injury crashes also increased from one in 2021 to five in 2022. Despite the rise in severe outcomes, crashes resulting in possible injuries decreased from 16 to 15.

Outcome by Severity (Crash Events)

Fatal4fatal crashes3.3%
Serious Injury5serious injury crashes4.1%
↑400.0%prior 1
Minor Injury7minor injury crashes5.8%
→0.0%prior 7
Possible Injury15possible injury crashes12.4%
↓-6.3%prior 16
No Injury90no injury crashes74.4%
↓-2.2%prior 92

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 leading contributing factor in both periods, with the count increasing from 23 crashes in 2021 to 26 in 2022. A notable shift occurred with 'Lost Control' incidents, which surged from just one crash in 2021 to nine in 2022, becoming the second-most common factor. 'Ran Stop Sign' incidents also increased slightly from eight to nine crashes year-over-year.

Officer-Reported Primary Contributing Cause

Animal26 (21.5%)↑13.0%prior 23
Lost Control9 (7.4%)
Ran Stop Sign9 (7.4%)↑12.5%prior 8
Driver Distraction: Other interior distraction8 (6.6%)↓-11.1%prior 9
Improper Backing8 (6.6%)↑33.3%prior 6
Followed too close7 (5.8%)
FTYROW: From stop sign5 (4.1%)↓-37.5%prior 8
Other (explain in narrative): Other4 (3.3%)
Ran off road - straight4 (3.3%)
Exceeded authorized speed3 (2.5%)

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 during daylight on dry, clear roads. However, the proportion of crashes in clear weather decreased from 62.1% of the total in 2021 to 52.1% in 2022. Concurrently, the share of crashes happening under cloudy skies increased from 15.5% to 24.0%. The distribution of crashes by road surface and lighting conditions remained stable between the two periods.

Weather

Clear63 (63.0%)
↓-12.5%prior 72
Cloudy29 (29.0%)
↑61.1%prior 18
Rain4 (4.0%)
↓-20.0%prior 5
Severe Winds2 (2.0%)
Freezing rain/drizzle1 (1.0%)
Snow1 (1.0%)

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

Lighting

Daylight74 (73.3%)
↑10.4%prior 67
Dark - roadway not lighted12 (11.9%)
↓-25.0%prior 16
Dark - roadway lighted8 (7.9%)
→0.0%prior 8
Dusk5 (5.0%)
↓-37.5%prior 8
Dawn1 (1.0%)
Dark - unknown roadway lighting1 (1.0%)

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

Road Surface

Dry78 (77.2%)
↑1.3%prior 77
Wet11 (10.9%)
↓-8.3%prior 12
Snow5 (5.0%)
Ice/frost3 (3.0%)
↓-40.0%prior 5
Gravel3 (3.0%)
Other (explain in narrative)1 (1.0%)

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 most common vehicle makes involved in crashes during both years. In 2022, Ford-made vehicles were involved in 44 incidents, while Chevrolet-badged vehicles were involved in 39, a reversal from 2021 when Chevrolets (48) were more numerous than Fords (25). Regarding persons involved, the 26-34 age group saw its count increase from 34 individuals in 2021 to 51 in 2022, while the 65+ age group remained the second-largest group in both periods.

Top Vehicle Makes (185 vehicles)

1
FORD44 (23.8%)
↑76.0%prior 25
2
CHEV25 (13.5%)
↑13.6%prior 22
3
CHEVROLET14 (7.6%)
↓-46.2%prior 26
4
NISS8 (4.3%)
5
GMC8 (4.3%)
↑14.3%prior 7
6
JEEP7 (3.8%)
↓-36.4%prior 11
7
DODG6 (3.2%)
↓-45.5%prior 11
8
BUIC5 (2.7%)
9
CHRY4 (2.2%)
↓-42.9%prior 7
10
MITS4 (2.2%)

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

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

Sex Distribution (163 persons with recorded sex)

Male94 (57.7%)
↑25.3%prior 75
Female69 (42.3%)
↓-13.8%prior 80

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: 121
  • Total persons involved: 258
  • Total vehicles involved: 185

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