Yearly Traffic Safety Analysis

112 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In 2023, Monroe County recorded 112 total vehicle crashes, a 1.8% decrease from the 114 crashes reported in 2022. During this period, the number of fatalities decreased from 3 to 2, and total injuries saw a slight reduction from 37 to 35. The most significant year-over-year change was the decrease in fatal crashes, which fell from 3 in 2022 to 2 in 2023.

112

▼ -1.8%was 114

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

35

▼ -5.4%was 37

Persons Injured

2

▼ -33.3%was 3

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 crash metrics in Monroe County remained relatively stable year-over-year, with a slight downward trend. Total crashes decreased by 1.8%, from 114 in 2022 to 112 in 2023. This trend extended to crash outcomes, with total fatalities dropping from 3 to 2, and total injuries decreasing from 37 to 35.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 2→0.0%

35

Motorists Injured

Prior: 34↑2.9%

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 timing of crashes showed some shifts between 2022 and 2023. While the peak hour for collisions remained 3 p.m. in both years (15 crashes in 2022, 12 in 2023), the most common day for crashes moved from Friday (23 crashes) in 2022 to Thursday (19 crashes) in 2023. In 2023, crashes were more evenly distributed across weekdays compared to the prior year, which saw a distinct peak on Fridays.

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 shifted year-over-year, with a decrease in the proportion of fatal incidents. Fatal crashes accounted for 1.8% of all crashes in 2023, down from 2.6% in 2022. While the overall share of injury-related crashes decreased from 27.2% to 22.4%, the distribution among injury types changed; serious injury crashes increased from 1 to 4, whereas minor injury crashes fell from 18 to 7. Consequently, the proportion of crashes resulting in no injuries increased from 70.2% in 2022 to 75.9% in 2023.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.8%
↓-33.3%prior 3
Serious Injury4serious injury crashes3.6%
↑300.0%prior 1
Minor Injury7minor injury crashes6.3%
↓-61.1%prior 18
Possible Injury14possible injury crashes12.5%
↑16.7%prior 12
No Injury85no injury crashes75.9%
↑6.3%prior 80

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 leading contributing factor in both periods, though the count decreased from 39 crashes in 2022 to 31 in 2023. Several other factors saw an increase in frequency. Crashes attributed to 'Followed too close' more than doubled, rising from 5 to 12, making it the second-most common factor in 2023. Similarly, incidents involving 'Driving too fast for conditions' increased from 5 to 9, and 'Operating vehicle in an reckless, erratic, careless, negligent manner' rose from 5 to 8.

Officer-Reported Primary Contributing Cause

Animal31 (27.7%)↓-20.5%prior 39
Followed too close12 (10.7%)↑140.0%prior 5
Driving too fast for conditions9 (8%)↑80.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner8 (7.1%)↑60.0%prior 5
FTYROW: From stop sign7 (6.3%)↑40.0%prior 5
Ran off road - straight7 (6.3%)↑40.0%prior 5
Lost Control6 (5.4%)↑20.0%prior 5
Ran off road - left5 (4.5%)
Driver Distraction: Other interior distraction5 (4.5%)
Other (explain in narrative): Other5 (4.5%)↓-44.4%prior 9

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

Road & Environmental Conditions

Crashes in both years predominantly occurred in clear weather and daylight on dry roads. In 2023, 72 crashes (64.3% of the total) happened on dry surfaces, compared to 61 (53.5%) in 2022. There was a notable year-over-year decrease in crashes on adverse road surfaces, which fell from 19 incidents in 2022 to 11 in 2023, driven primarily by a drop in crashes on wet roads from 10 to 4. Crashes in dark conditions remained stable, with 22 in 2023 and 23 in 2022.

Weather

Clear69 (79.3%)
↑7.8%prior 64
Cloudy10 (11.5%)
→0.0%prior 10
Snow4 (4.6%)
Rain3 (3.4%)
↓-40.0%prior 5
Freezing rain/drizzle1 (1.1%)

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

Lighting

Daylight59 (67.8%)
↑11.3%prior 53
Dark - roadway lighted12 (13.8%)
↑100.0%prior 6
Dark - roadway not lighted10 (11.5%)
↓-37.5%prior 16
Dawn5 (5.7%)
↓-54.5%prior 11
Dusk1 (1.1%)

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

Road Surface

Dry72 (82.8%)
↑18.0%prior 61
Gravel4 (4.6%)
↓-33.3%prior 6
Snow4 (4.6%)
Wet4 (4.6%)
↓-60.0%prior 10
Slush2 (2.3%)
Ice/frost1 (1.1%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes were consistent year-over-year, with Ford (36 vehicles in 2023 vs. 39 in 2022) and Chevrolet (34 vs. 39) being the most common. The demographic profile of individuals involved showed some changes. While the 35-44 age group was the most represented in both years with 51 individuals, the number of persons aged 16-20 involved in crashes increased from 33 in 2022 to 45 in 2023. Conversely, involvement for the 55-64 age group decreased from 42 to 30 persons.

Top Vehicle Makes (170 vehicles)

1
FORD36 (21.2%)
↓-7.7%prior 39
2
CHEV26 (15.3%)
↓-3.7%prior 27
3
JEEP10 (5.9%)
↑25.0%prior 8
4
DODG8 (4.7%)
↑33.3%prior 6
5
CHEVROLET8 (4.7%)
↓-33.3%prior 12
6
NISS7 (4.1%)
7
GMC7 (4.1%)
→0.0%prior 7
8
TOYOTA6 (3.5%)
9
BUIC5 (2.9%)
10
TOYO5 (2.9%)

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

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

Sex Distribution (161 persons with recorded sex)

Male98 (60.9%)
↑2.1%prior 96
Female63 (39.1%)
↑1.6%prior 62

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: 241
  • Total vehicles involved: 170

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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