Yearly Traffic Safety Analysis

192 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In 2023, Monona County recorded 192 total traffic crashes, a 25.5% increase from the 153 crashes reported in 2022. This period also saw total injuries rise from 50 to 68. The most significant year-over-year change was the occurrence of one fatal crash resulting in one fatality in 2023, whereas there were no fatalities in the prior year.

192

▲ 25.5%was 153

Total Crash Events

1

Persons Killed

68

▲ 36.0%was 50

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) 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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Monona County indicates a rising trend year-over-year. Total crashes increased by 39, from 153 in 2022 to 192 in 2023. This increase was accompanied by a 36% rise in injuries, from 50 to 68, and the introduction of one fatality in 2023 compared to zero in 2022.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 0→%

68

Motorists Injured

Prior: 50↑36.0%

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 day for crashes was Wednesday with 32 incidents, a change from 2022 when Friday was the peak day with 26 crashes. The peak hour remained consistent at 2 p.m. in both years, though the volume in that hour increased from 12 crashes in 2022 to 16 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

Crash severity worsened in 2023 compared to 2022. The county recorded one fatal crash in 2023, resulting in a fatal crash rate of 0.52%, up from zero fatal crashes in the previous year. The count of serious injury crashes more than doubled from 6 to 13, with their share of all crashes increasing from 3.9% in 2022 to 6.8% in 2023. Conversely, the proportion of crashes with no injuries decreased slightly from 70.6% to 69.3%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
Serious Injury13serious injury crashes6.8%
↑116.7%prior 6
Minor Injury26minor injury crashes13.5%
↑62.5%prior 16
Possible Injury19possible injury crashes9.9%
↓-17.4%prior 23
No Injury133no injury crashes69.3%
↑23.1%prior 108

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

The leading contributing factors remained consistent in rank but grew in volume year-over-year. Collisions involving an animal were the top factor in both periods, with the count increasing by 65.4% from 26 in 2022 to 43 in 2023. 'Lost Control' remained the second-most cited factor, with its count increasing by 66.7% from 18 to 30 incidents. 'Driving too fast for conditions' also saw an increase, from 10 crashes in 2022 to 13 in 2023.

Officer-Reported Primary Contributing Cause

Animal43 (22.4%)↑65.4%prior 26
Lost Control30 (15.6%)↑66.7%prior 18
Driving too fast for conditions13 (6.8%)↑30.0%prior 10
Ran off road - straight10 (5.2%)→0.0%prior 10
Followed too close9 (4.7%)↓-10.0%prior 10
Ran off road - left8 (4.2%)↓-27.3%prior 11
Other (explain in narrative): Other7 (3.6%)↓-22.2%prior 9
Ran Stop Sign7 (3.6%)
Driver Distraction: Other interior distraction7 (3.6%)
FTYROW: At uncontrolled intersection5 (2.6%)

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

Road & Environmental Conditions

There was a notable shift in crash conditions year-over-year. The number of crashes occurring in the dark on unlit roadways nearly doubled, from 25 in 2022 to 46 in 2023, increasing its share of total crashes from 16.3% to 24.0%. Similarly, the count of crashes on wet road surfaces tripled from 5 to 15. The proportion of crashes occurring in clear weather and on dry roads remained relatively stable across both periods.

Weather

Clear117 (72.7%)
↑18.2%prior 99
Cloudy20 (12.4%)
↑42.9%prior 14
Rain9 (5.6%)
Snow8 (5.0%)
↓-20.0%prior 10
Fog, smoke, smog3 (1.9%)
Freezing rain/drizzle3 (1.9%)
Blowing Snow1 (0.6%)

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

Lighting

Daylight98 (60.9%)
↑5.4%prior 93
Dark - roadway not lighted46 (28.6%)
↑84.0%prior 25
Dark - roadway lighted6 (3.7%)
Dawn6 (3.7%)
Dusk4 (2.5%)
↓-33.3%prior 6
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry119 (73.9%)
↑25.3%prior 95
Wet15 (9.3%)
↑200.0%prior 5
Snow7 (4.3%)
↓-12.5%prior 8
Ice/frost7 (4.3%)
↓-36.4%prior 11
Gravel7 (4.3%)
↓-41.7%prior 12
Slush4 (2.5%)
Water (standing or moving)1 (0.6%)
Sand1 (0.6%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Ford and Chevrolet, remained consistent across both years, with both showing an increase in total counts. A significant demographic shift occurred in the age of persons involved in crashes; the 26-34 age group saw its involvement nearly double from 35 individuals in 2022 to 66 in 2023. Substantial increases were also observed for the 45-54, 55-64, and 65+ age groups.

Top Vehicle Makes (271 vehicles)

1
FORD48 (17.7%)
↑37.1%prior 35
2
CHEV36 (13.3%)
↑5.9%prior 34
3
CHEVROLET17 (6.3%)
→0.0%prior 17
4
GMC12 (4.4%)
↑20.0%prior 10
5
DODGE10 (3.7%)
↑66.7%prior 6
6
KIA9 (3.3%)
7
NR7 (2.6%)
↑40.0%prior 5
8
KENWORTH7 (2.6%)
9
DODG7 (2.6%)
↓-22.2%prior 9
10
TOYOTA6 (2.2%)
→0.0%prior 6

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

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

Sex Distribution (242 persons with recorded sex)

Male160 (66.1%)
↑20.3%prior 133
Female82 (33.9%)
↑26.2%prior 65

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: 192
  • Total persons involved: 386
  • Total vehicles involved: 271

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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