Yearly Traffic Safety Analysis

281 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Harrison County, there were 281 total crashes in 2024, a 2.4% decrease from the 288 crashes recorded in 2023. The most significant year-over-year change was the reduction in fatalities, which dropped from 7 in the prior period to 0 in the current period. Total injuries also decreased from 94 to 78.

281

▼ -2.4%was 288

Total Crash Events

0

▼ -100.0%was 7

Persons Killed

78

▼ -17.0%was 94

Persons Injured

0

▼ -100.0%was 4

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, Harrison County experienced a downward trend in traffic crashes, with total incidents decreasing by 2.4% from 288 in 2023 to 281 in 2024. This trend extended to crash outcomes, as total injuries fell by 17.0% from 94 to 78, and fatalities were eliminated, dropping from 7 to 0.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 7↓-100.0%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

76

Motorists Injured

Prior: 94↓-19.1%

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes in Harrison County showed some shifts between the two periods. The peak day for crashes moved from Saturday (54 crashes) in 2023 to Friday (53 crashes) in 2024. The peak hour for collisions also shifted earlier, from 6 p.m. in the prior period (24 crashes) to 4 p.m. in the current period (26 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity improved significantly, with fatal crashes decreasing from 4 in 2023 to 0 in 2024. While total injuries declined, the number of serious injury crashes increased from 11 to 16. However, crashes resulting in minor injuries fell from 33 to 27, and possible injury crashes decreased from 36 to 20. The proportion of crashes with no injuries increased from 70.8% in the prior period to 77.6% in the current period.

Outcome by Severity (Crash Events)

Serious Injury16serious injury crashes5.7%
↑45.5%prior 11
Minor Injury27minor injury crashes9.6%
↓-18.2%prior 33
Possible Injury20possible injury crashes7.1%
↓-44.4%prior 36
No Injury218no injury crashes77.6%
↑6.9%prior 204

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes remained consistent year-over-year. Collisions involving an 'Animal' continued to be the most common factor, though the count decreased slightly from 53 to 51. 'Lost Control' remained a top factor, with its count dropping from 45 to 36. 'Ran off road - left' crashes increased in count from 24 to 29, moving up to the third-ranked factor, while 'Ran off road - straight' incidents decreased from 27 to 25.

Officer-Reported Primary Contributing Cause

Animal51 (18.1%)↓-3.8%prior 53
Lost Control36 (12.8%)↓-20.0%prior 45
Ran off road - left29 (10.3%)↑20.8%prior 24
Ran off road - straight25 (8.9%)↓-7.4%prior 27
Followed too close22 (7.8%)↑10.0%prior 20
Driving too fast for conditions17 (6%)↓-5.6%prior 18
Driver Distraction: Other interior distraction14 (5%)↑75.0%prior 8
Driver Distraction: Inattentive/lost in thought9 (3.2%)↑80.0%prior 5
Other (explain in narrative): Other8 (2.8%)↑60.0%prior 5
FTYROW: From stop sign6 (2.1%)↓-60.0%prior 15

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across environmental conditions remained broadly stable year-over-year, with a majority of incidents in both periods occurring in clear weather on dry roads. Crashes during daylight hours decreased from 176 to 151, representing a drop in share from 61.1% to 53.7% of all crashes. Correspondingly, crashes in dark, unlighted conditions increased from 39 to 54. The number of crashes on icy or frosty road surfaces saw a slight increase from 19 to 23.

Weather

Clear167 (70.5%)
↓-4.6%prior 175
Cloudy31 (13.1%)
→0.0%prior 31
Snow12 (5.1%)
↓-20.0%prior 15
Freezing rain/drizzle7 (3.0%)
↑40.0%prior 5
Fog, smoke, smog6 (2.5%)
Rain6 (2.5%)
↓-33.3%prior 9
Blowing Snow4 (1.7%)
Severe Winds2 (0.8%)
Sleet, hail1 (0.4%)
Other (explain in narrative)1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight151 (61.6%)
↓-14.2%prior 176
Dark - roadway not lighted54 (22.0%)
↑38.5%prior 39
Dark - roadway lighted19 (7.8%)
↑18.8%prior 16
Dawn9 (3.7%)
↑80.0%prior 5
Dusk6 (2.4%)
Dark - unknown roadway lighting6 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry174 (72.8%)
↓-2.2%prior 178
Ice/frost23 (9.6%)
↑21.1%prior 19
Snow16 (6.7%)
↑23.1%prior 13
Gravel13 (5.4%)
↑62.5%prior 8
Wet11 (4.6%)
↓-31.3%prior 16
Slush1 (0.4%)
Other (explain in narrative)1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The types of vehicles involved in crashes were consistent, with Ford (65 current vs. 67 prior) and Chevrolet (combining 'CHEV' and 'CHEVROLET' gives 70 current vs. 72 prior) remaining the most common makes in both years. Analysis of persons involved shows a decrease across most age groups, corresponding to the lower number of total persons involved (407 vs. 569). The 16-20 age group saw a notable reduction in involvement, from 72 individuals in 2023 to 44 in 2024. Similarly, the 35-44 age group decreased from 106 to 67 persons.

Top Vehicle Makes (387 vehicles)

1
FORD65 (16.8%)
↓-3.0%prior 67
2
CHEV47 (12.1%)
↓-2.1%prior 48
3
CHEVROLET23 (5.9%)
↓-4.2%prior 24
4
JEEP20 (5.2%)
↑81.8%prior 11
5
DODG20 (5.2%)
↑17.6%prior 17
6
GMC14 (3.6%)
↑27.3%prior 11
7
NISS13 (3.4%)
↑8.3%prior 12
8
PETERBILT12 (3.1%)
↑20.0%prior 10
9
DODGE10 (2.6%)
↑42.9%prior 7
10
KIA10 (2.6%)
↓-9.1%prior 11

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (247 persons with recorded sex)

Male159 (64.4%)
↓-37.2%prior 253
Female88 (35.6%)
↓-26.1%prior 119

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 281
  • Total persons involved: 407
  • Total vehicles involved: 387

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: 2024." Published September 9, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2024-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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