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Yearly Traffic Safety Analysis

158 CRASHES IN
GENOA, OH
2024

All metrics benchmarked against2023

Overall, total crashes in Genoa saw a slight increase of 1.94%, rising from 155 in the prior year to 158 in the current year. The most notable year-over-year shift was a 160% increase in DUI crashes, which rose from 5 to 13.

158

1.9%was 155

Total Crash Events

1

Persons Killed

72

26.3%was 57

Persons Injured

15

36.4%was 11

Hit-and-Run Crashes

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: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a slight increase in total crashes, rising by 1.94% from 155 to 158. While total fatalities remained stable at 1, total injuries increased by 26.32%, from 57 to 72.

15

Hit-and-Run Crashes — 2024

36.4% vs prior (11)

Hit-and-run crashes increased from 11 in the prior period to 15 in the current period. This resulted in an upward trend for the hit-and-run rate, which rose from 7.1% to 9.5% year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

1

Motorists Killed

Prior: 10.0%

2

Pedestrians Injured

Prior: 0%

70

Motorists Injured

Prior: 5722.8%

Source: Ohio Crash Data (ODOT TIMS) · Csv 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 peak day for crashes shifted from Wednesday, with 33 crashes in the prior year, to Friday, with 35 crashes in the current year. Similarly, the peak crash hour moved from 3 PM (16 crashes) in the prior year to 7 AM (17 crashes) in the current year.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The number of fatal crashes remained constant at 1 in both periods, with the fatal crash rate decreasing slightly from 0.65% to 0.63%. Total injuries increased by 26.32%, rising from 57 to 72, with serious injury crashes (severity A) doubling from 1 to 2.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.6%
0.0%prior 1
Serious Injury2serious injury crashes1.3%
100.0%prior 1
Minor Injury26minor injury crashes16.5%
-16.1%prior 31
Possible Injury16possible injury crashes10.1%
77.8%prior 9
No Injury113no injury crashes71.5%
0.0%prior 113

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes on dry road surfaces increased from 108 to 116, while those on wet road surfaces decreased from 38 to 32, and on ice from 4 to 1. Crashes occurring in 'Dark - Lighted Roadway' conditions saw a notable increase, rising from 2 in the prior year to 9 in the current year.

Weather

Clear92 (58.2%)
1.1%prior 91
Cloudy31 (19.6%)
14.8%prior 27
Rain23 (14.6%)
-4.2%prior 24
Snow10 (6.3%)
-9.1%prior 11
Fog; Smog; Smoke1 (0.6%)
Other/Unknown1 (0.6%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight111 (70.3%)
-2.6%prior 114
Dark - Roadway Not Lighted26 (16.5%)
-7.1%prior 28
Dawn/Dusk11 (7.0%)
10.0%prior 10
Dark - Lighted Roadway9 (5.7%)
Other/Unknown1 (0.6%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry116 (73.4%)
7.4%prior 108
Wet32 (20.3%)
-15.8%prior 38
Snow9 (5.7%)
80.0%prior 5
Ice1 (0.6%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

Sport Utility Vehicles became the most involved vehicle type, increasing from 81 to 107, while Passenger Cars decreased from 125 to 102. The age groups 0-15 and 65+ experienced significant increases in involved persons, rising from 18 to 38 and 26 to 47 respectively.

Top Vehicle Makes (279 vehicles)

1
HONDA44 (15.8%)
-20.0%prior 55
2
FORD34 (12.2%)
3.0%prior 33
3
CHEVROLET30 (10.8%)
7.1%prior 28
4
TOYOTA25 (9%)
-10.7%prior 28
5
JEEP19 (6.8%)
90.0%prior 10
6
GMC12 (4.3%)
-14.3%prior 14
7
KIA10 (3.6%)
66.7%prior 6
8
NISSAN10 (3.6%)
42.9%prior 7
9
DODGE9 (3.2%)
50.0%prior 6
10
VOLVO7 (2.5%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (346 persons with recorded sex)

Male201 (58.1%)
12.9%prior 178
Female145 (41.9%)
-2.0%prior 148

Source: Ohio Crash Data (ODOT TIMS) · Csv 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 Ohio Crash Data (ODOT TIMS), accessed programmatically via the Csv 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: Csv 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: August 22, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: Genoa, OH
  • Total crash records analyzed: 158
  • Total persons involved: 353
  • Total vehicles involved: 279

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). "Genoa, OH Crash Intelligence Report: 2024." Published August 22, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/genoa/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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