Yearly Traffic Safety Analysis

263 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Harrison County, total vehicle crashes decreased by 13.5% from 304 in 2021 to 263 in 2022. While the number of fatalities slightly increased from 4 to 5, there was a significant 41% reduction in total injuries, which fell from 122 to 72 year-over-year. The most notable shift was this substantial decline in non-fatal injuries, with serious injury crashes dropping from 20 incidents in the prior year to just 5 in the current year.

263

▼ -13.5%was 304

Total Crash Events

5

▲ 25.0%was 4

Persons Killed

72

▼ -41.0%was 122

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (5) 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 traffic crash trends in Harrison County show a notable improvement year-over-year. The total number of crashes declined from 304 in 2021 to 263 in 2022, representing a 13.5% decrease. This equates to 41 fewer crash incidents in the current period compared to the prior year.

Vulnerable Road User Casualties

5

Motorists Killed

Prior: 4↑25.0%

72

Motorists Injured

Prior: 122↓-41.0%

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 timing of crashes shifted between the two periods. In 2022, the peak day for crashes was Monday with 42 incidents, a change from 2021 when Friday was the peak day with 48 incidents. Similarly, the peak hour for crashes moved slightly earlier, from 7 p.m. (22 crashes) in 2021 to 6 p.m. (18 crashes) in 2022.

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

While the number of fatal crashes remained unchanged at 4 for both 2021 and 2022, the total number of fatalities increased from 4 to 5. Despite this, the overall severity of crashes decreased significantly, with serious injury crashes dropping from 20 to 5. Consequently, crashes resulting in no injuries made up a larger proportion of the total, increasing from 66.8% in 2021 to 73.4% in 2022.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.5%
→0.0%prior 4
Serious Injury5serious injury crashes1.9%
↓-75.0%prior 20
Minor Injury25minor injury crashes9.5%
↓-21.9%prior 32
Possible Injury36possible injury crashes13.7%
↓-20.0%prior 45
No Injury193no injury crashes73.4%
↓-4.9%prior 203

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 involving animals remained the leading contributing factor in both periods, though the count of such incidents decreased by nearly 30%, from 74 in 2021 to 52 in 2022. The number of crashes attributed to 'Driving too fast for conditions' was more than halved, falling from 31 incidents to 15. 'Lost Control' remained the second-highest factor in both years, with a slight decrease in count from 36 to 33.

Officer-Reported Primary Contributing Cause

Animal52 (19.8%)↓-29.7%prior 74
Lost Control33 (12.5%)↓-8.3%prior 36
Ran off road - straight23 (8.7%)↓-20.7%prior 29
Ran off road - left18 (6.8%)↑50.0%prior 12
Driving too fast for conditions15 (5.7%)↓-51.6%prior 31
Other (explain in narrative): Other13 (4.9%)↑30.0%prior 10
Followed too close13 (4.9%)↑8.3%prior 12
FTYROW: From stop sign8 (3%)↓-27.3%prior 11
Exceeded authorized speed7 (2.7%)↓-22.2%prior 9
Improper Backing6 (2.3%)↓-25.0%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Year-over-year, a greater proportion of crashes occurred in clear and dry conditions. Crashes on dry road surfaces accounted for 65.4% of the total in 2022, up from a 57.9% share in 2021. Correspondingly, crashes on wet roads saw a notable drop in count from 24 to 13. Similarly, crashes in daylight represented a larger share of the total in 2022 (54.8%) compared to 2021 (50.0%).

Weather

Clear162 (73.6%)
↓-5.8%prior 172
Cloudy28 (12.7%)
↓-17.6%prior 34
Snow14 (6.4%)
↑7.7%prior 13
Rain7 (3.2%)
↓-30.0%prior 10
Blowing Snow4 (1.8%)
↓-20.0%prior 5
Freezing rain/drizzle2 (0.9%)
Fog, smoke, smog2 (0.9%)
Other (explain in narrative)1 (0.5%)

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

Lighting

Daylight144 (64.9%)
↓-5.3%prior 152
Dark - roadway not lighted55 (24.8%)
↓-19.1%prior 68
Dawn10 (4.5%)
↑66.7%prior 6
Dark - roadway lighted10 (4.5%)
↑25.0%prior 8
Dusk3 (1.4%)
↓-57.1%prior 7

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

Road Surface

Dry172 (77.1%)
↓-2.3%prior 176
Ice/frost16 (7.2%)
↑6.7%prior 15
Wet13 (5.8%)
↓-45.8%prior 24
Snow12 (5.4%)
↓-20.0%prior 15
Gravel5 (2.2%)
↓-54.5%prior 11
Mud, dirt2 (0.9%)
Slush1 (0.4%)
Oil1 (0.4%)
Other (explain in narrative)1 (0.4%)

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

Vehicles & Demographics

Ford and Chevrolet-branded vehicles were the most common makes involved in crashes in both years, though the count for Ford vehicles decreased from 79 to 56. The number of persons involved in crashes decreased across most age groups, reflecting the overall decline in incidents. The 16-20 age group saw a reduction from 73 persons involved in 2021 to 49 in 2022, and the 35-44 age group saw a decrease from 93 to 70.

Top Vehicle Makes (358 vehicles)

1
FORD56 (15.6%)
↓-29.1%prior 79
2
CHEV54 (15.1%)
↑28.6%prior 42
3
CHEVROLET28 (7.8%)
↓-28.2%prior 39
4
JEEP17 (4.7%)
↑142.9%prior 7
5
HOND13 (3.6%)
↑160.0%prior 5
6
NISS11 (3.1%)
7
RAM10 (2.8%)
8
BUIC9 (2.5%)
↑50.0%prior 6
9
HONDA8 (2.2%)
↓-50.0%prior 16
10
DODG8 (2.2%)
↓-38.5%prior 13

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

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

Sex Distribution (337 persons with recorded sex)

Male223 (66.2%)
↑3.7%prior 215
Female114 (33.8%)
↑8.6%prior 105

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: 263
  • Total persons involved: 494
  • Total vehicles involved: 358

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