Yearly Traffic Safety Analysis

147 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Palo Alto County, there were 147 total crashes in 2023, a slight increase from 145 crashes in 2022, representing a 1.4% rise. Fatalities remained unchanged with one death recorded in each period. The most notable year-over-year shift was in the temporal patterns of crashes, with the peak day for collisions moving from Wednesday in 2022 to Thursday in 2023, and the peak hour shifting from the evening (7 p.m.) to the morning (7 a.m.).

147

▲ 1.4%was 145

Total Crash Events

1

Persons Killed

42

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

Overall traffic crash trends in Palo Alto County remained relatively stable year-over-year. The total number of crashes increased by just two incidents, from 145 in 2022 to 147 in 2023, a 1.4% increase. The number of injuries and fatalities was identical in both periods, with 42 injuries and one fatality each year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

2

Cyclists Injured

Prior: 0→%

40

Motorists Injured

Prior: 42↓-4.8%

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 timing of crashes saw a distinct shift between the two periods. In 2023, Thursday was the most frequent day for crashes with 34 incidents, a significant change from 2022 when Wednesday was the peak day with 27 crashes. Similarly, the peak hour for crashes moved from 7 p.m. in 2022 (12 crashes) to 7 a.m. in 2023 (12 crashes), indicating a shift from evening to morning commute times.

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 levels were consistent year-over-year. Both 2023 and 2022 recorded one fatal crash, resulting in one fatality each year, with the fatal crash rate holding steady at approximately 0.7%. The total number of crashes involving any level of injury was 34 in both periods. Within the injury categories, there was a minor shift: minor injury crashes increased from 14 to 16, while possible injury crashes decreased from 17 to 15.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
→0.0%prior 1
Serious Injury3serious injury crashes2%
→0.0%prior 3
Minor Injury16minor injury crashes10.9%
↑14.3%prior 14
Possible Injury15possible injury crashes10.2%
↓-11.8%prior 17
No Injury112no injury crashes76.2%
↑1.8%prior 110

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 with animals remained the top contributing factor in both periods, accounting for 50 crashes in 2023 compared to 51 in 2022. While the top factor's rank and count were stable, other factors saw significant changes. Crashes attributed to 'Lost Control' decreased from 10 to 6, and those from 'Driving too fast for conditions' fell from 9 to 5. Conversely, crashes where 'Followed too close' was a factor more than tripled, increasing from 2 in 2022 to 7 in 2023.

Officer-Reported Primary Contributing Cause

Animal50 (34%)↓-2.0%prior 51
Followed too close7 (4.8%)
FTYROW: From stop sign6 (4.1%)↑20.0%prior 5
Lost Control6 (4.1%)↓-40.0%prior 10
Driving too fast for conditions5 (3.4%)↓-44.4%prior 9
FTYROW: From driveway5 (3.4%)↓-28.6%prior 7
Operating vehicle in an reckless, erratic, careless, negligent manner4 (2.7%)
FTYROW: At uncontrolled intersection4 (2.7%)↓-33.3%prior 6
Ran off road - left4 (2.7%)↓-20.0%prior 5
Ran off road - straight4 (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

There was a shift toward crashes occurring in more favorable conditions in 2023 compared to 2022. Crashes in clear weather increased from 56 to 70, and those on dry road surfaces rose from 56 to 65. Correspondingly, crashes on roads with ice or frost were halved, decreasing from 10 incidents in 2022 to 5 in 2023. Crashes during daylight hours also saw an increase, from 66 in 2022 to 78 in 2023.

Weather

Clear70 (68.6%)
↑25.0%prior 56
Cloudy17 (16.7%)
↓-34.6%prior 26
Snow8 (7.8%)
↑60.0%prior 5
Freezing rain/drizzle2 (2.0%)
Fog, smoke, smog1 (1.0%)
Rain1 (1.0%)
Severe Winds1 (1.0%)
Sleet, hail1 (1.0%)
Blowing Snow1 (1.0%)

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

Lighting

Daylight78 (76.5%)
↑18.2%prior 66
Dark - roadway not lighted14 (13.7%)
↓-17.6%prior 17
Dark - roadway lighted5 (4.9%)
↓-28.6%prior 7
Dusk5 (4.9%)

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

Road Surface

Dry65 (63.7%)
↑16.1%prior 56
Snow15 (14.7%)
↑36.4%prior 11
Wet7 (6.9%)
↓-30.0%prior 10
Ice/frost5 (4.9%)
↓-50.0%prior 10
Gravel5 (4.9%)
↓-28.6%prior 7
Slush4 (3.9%)
Other (explain in narrative)1 (1.0%)

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 showed a change at the top, with Ford becoming the most frequently involved make in 2023 with 51 vehicles, up from 35 in the prior year. Chevrolet (recorded as 'CHEV') was the top make in 2022 with 45 vehicles but saw its involvement decrease to 38 in 2023. Analysis of persons involved shows an increase in younger individuals, with those in the 16-20 age group increasing from 37 to 45 and the 21-25 age group increasing from 18 to 27.

Top Vehicle Makes (220 vehicles)

1
FORD51 (23.2%)
↑45.7%prior 35
2
CHEV38 (17.3%)
↓-15.6%prior 45
3
CHEVROLET13 (5.9%)
↑116.7%prior 6
4
GMC12 (5.5%)
↑9.1%prior 11
5
DODG10 (4.5%)
↑66.7%prior 6
6
JEEP7 (3.2%)
↓-22.2%prior 9
7
BUIC6 (2.7%)
↑20.0%prior 5
8
PONT6 (2.7%)
9
CHRY5 (2.3%)
↓-16.7%prior 6
10
PTRB5 (2.3%)

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 (210 persons with recorded sex)

Male131 (62.4%)
↑8.3%prior 121
Female79 (37.6%)
↑14.5%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: 147
  • Total persons involved: 315
  • Total vehicles involved: 220

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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