Yearly Traffic Safety Analysis

112 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In 2023, Montgomery County recorded 112 total crashes, a 7.4% decrease from the 121 crashes documented in 2022. While total collisions declined, the number of people injured increased by 40%, from 25 to 35. The most notable year-over-year shift was a 50% reduction in fatalities, which fell from 4 in the prior year to 2 in 2023.

112

▼ -7.4%was 121

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

35

▲ 40.0%was 25

Persons Injured

2

▼ -50.0%was 4

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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Montgomery County saw a modest decline in 2023, falling by 7.4% from 121 incidents in the prior year to 112. While total crashes and fatalities (down 50% from 4 to 2) decreased, the number of people injured in these incidents rose by 40%, from 25 in 2022 to 35 in 2023.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 2→0.0%

35

Motorists Injured

Prior: 24↑45.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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 2023, the peak days for crashes were Thursday and Saturday, each with 21 incidents, a change from 2022 when Friday was the most common day with 27 crashes. The peak hour for collisions also shifted earlier in the day, moving from 4 p.m. (11 crashes) in 2022 to 1 p.m. (11 crashes) in 2023.

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes showed a mixed trend in 2023 compared to the previous year. Fatal crashes dropped from 4 incidents (3.3% of all crashes) in 2022 to 2 incidents (1.8%) in 2023. Crashes resulting in serious injuries also saw a significant reduction, falling from 5 in the prior year to just 1 in the current period. Conversely, the count of minor injury crashes more than doubled, increasing from 7 in 2022 to 17 in 2023.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.8%
↓-50.0%prior 4
Serious Injury1serious injury crashes0.9%
↓-80.0%prior 5
Minor Injury17minor injury crashes15.2%
↑142.9%prior 7
Possible Injury8possible injury crashes7.1%
↓-46.7%prior 15
No Injury84no injury crashes75%
↓-6.7%prior 90

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both years, with 25 incidents in 2023, nearly unchanged from 26 in 2022. The second-ranked factor shifted; "FTYROW: At uncontrolled intersection" was cited in 9 crashes in 2023, while "Lost Control" and "Ran Stop Sign" were tied for second in 2022 with 9 crashes each. The count for "Ran Stop Sign" incidents decreased by 44%, from 9 in 2022 to 5 in 2023.

Officer-Reported Primary Contributing Cause

Animal25 (22.3%)↓-3.8%prior 26
FTYROW: At uncontrolled intersection9 (8%)
FTYROW: From stop sign8 (7.1%)↑60.0%prior 5
Lost Control7 (6.3%)↓-22.2%prior 9
Made improper turn5 (4.5%)
Ran Stop Sign5 (4.5%)↓-44.4%prior 9
Ran off road - straight4 (3.6%)
Operating vehicle in an reckless, erratic, careless, negligent manner4 (3.6%)
Followed too close3 (2.7%)↓-57.1%prior 7
FTYROW: From driveway3 (2.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The environmental conditions under which crashes occurred remained largely consistent between 2022 and 2023. In both periods, the majority of incidents happened in "Clear" weather (62 in 2023 vs. 63 in 2022) and during "Daylight" hours (74 incidents in both years). Similarly, "Dry" road surfaces were reported in most crashes, with 76 incidents in 2023 compared to 78 in 2022, indicating no significant year-over-year change in the role of adverse conditions.

Weather

Clear62 (64.6%)
↓-1.6%prior 63
Cloudy27 (28.1%)
↓-6.9%prior 29
Rain4 (4.2%)
Blowing Snow2 (2.1%)
Freezing rain/drizzle1 (1.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight74 (77.1%)
→0.0%prior 74
Dark - roadway not lighted13 (13.5%)
↑8.3%prior 12
Dark - roadway lighted7 (7.3%)
↓-12.5%prior 8
Dawn1 (1.0%)
Dusk1 (1.0%)
↓-80.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry76 (79.2%)
↓-2.6%prior 78
Wet10 (10.4%)
↓-9.1%prior 11
Slush4 (4.2%)
Mud, dirt2 (2.1%)
Ice/frost2 (2.1%)
Snow2 (2.1%)
↓-60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet were the top two vehicle makes involved in crashes in both years, though the number of Fords decreased from 44 in 2022 to 34 in 2023. A significant demographic shift occurred among persons involved in crashes; the 65+ age group saw a 44% increase in involvement, rising from 36 individuals in 2022 to 52 in 2023, making it the most represented age group. Conversely, involvement for the 26-34 age group fell by 57%, from 51 individuals in 2022 to 22 in 2023.

Top Vehicle Makes (178 vehicles)

1
FORD34 (19.1%)
↓-22.7%prior 44
2
CHEV30 (16.9%)
↑20.0%prior 25
3
JEEP11 (6.2%)
↑57.1%prior 7
4
DODG9 (5.1%)
↑50.0%prior 6
5
BUIC8 (4.5%)
↑60.0%prior 5
6
HYUN7 (3.9%)
7
GMC7 (3.9%)
↓-12.5%prior 8
8
CHEVROLET6 (3.4%)
↓-57.1%prior 14
9
KIA5 (2.8%)
10
CHRY5 (2.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (163 persons with recorded sex)

Male100 (61.3%)
↑6.4%prior 94
Female63 (38.7%)
↓-8.7%prior 69

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 112
  • Total persons involved: 244
  • Total vehicles involved: 178

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: 2023." Published September 9, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2023-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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