Yearly Traffic Safety Analysis

3,166 CRASHES IN
OHIO, OH
2023

All metrics benchmarked against2022

In 2023, Greene County recorded 3,166 total traffic crashes, a 6.1% decrease from the 3,373 crashes reported in 2022. The most significant year-over-year change was a substantial reduction in traffic fatalities, which fell from 18 in 2022 to 5 in 2023. Total injuries also saw a decline, from 1,166 to 1,029.

3,166

▼ -6.1%was 3,373

Total Crash Events

5

▼ -72.2%was 18

Persons Killed

1,029

▼ -11.7%was 1,166

Persons Injured

391

▼ -14.4%was 457

Hit-and-Run Crashes

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Greene County showed a downward trend from 2022 to 2023. The total number of crashes decreased by 6.1%, from 3,373 to 3,166. This decline was accompanied by an 11.8% reduction in total injuries and a 72.2% drop in fatalities year-over-year.

391

Hit-and-Run Crashes — 2023

▼ -14.4% vs prior (457)

Hit-and-run incidents decreased in both absolute numbers and as a percentage of total crashes. In 2023, there were 391 hit-and-run crashes, down from 457 in 2022. This represents a decline in the hit-and-run rate from 13.5% of all crashes in the prior year to 12.3% in the current year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 3↓-100.0%

5

Motorists Killed

Prior: 15↓-66.7%

12

Pedestrians Injured

Prior: 15↓-20.0%

1,017

Motorists Injured

Prior: 1,151↓-11.6%

Source: Ohio Crash Data (ODOT TIMS) · Csv 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 temporal patterns of crashes remained broadly consistent year-over-year. Friday was the peak day for crashes in both 2023 (538 crashes) and 2022 (581 crashes). However, the peak hour for collisions shifted slightly, moving from the 5 p.m. hour in 2022 (294 crashes) to the 4 p.m. hour in 2023 (275 crashes).

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased notably from 2022 to 2023, driven by a sharp drop in fatal incidents. The number of fatal crashes fell from 15 to 5, causing the fatal crash rate to decline from 0.44% to 0.16%. The proportion of crashes resulting in serious injuries remained stable at 2.4% in both years, while crashes with no injuries increased from 75.5% to 76.8% of all incidents.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.2%
↓-66.7%prior 15
Serious Injury75serious injury crashes2.4%
↓-6.3%prior 80
Minor Injury405minor injury crashes12.8%
↓-10.6%prior 453
Possible Injury250possible injury crashes7.9%
↓-10.4%prior 279
No Injury2,431no injury crashes76.8%
↓-4.5%prior 2,546

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather and on dry roads. In 2023, the proportion of crashes on dry roads increased to 79.2% from 75.2% in 2022. There was a notable year-over-year reduction in crashes occurring in adverse winter conditions; incidents on snowy or icy roads accounted for 3.0% of the total in 2023, down from 6.3% in 2022. Similarly, crashes during snowy weather fell from 173 incidents to 85.

Weather

Clear2,008 (63.4%)
↓-1.6%prior 2,040
Cloudy711 (22.5%)
↓-2.7%prior 731
Rain311 (9.8%)
↓-9.1%prior 342
Snow85 (2.7%)
↓-50.9%prior 173
Other/Unknown26 (0.8%)
↓-33.3%prior 39
Fog; Smog; Smoke12 (0.4%)
↓-33.3%prior 18
Severe Crosswinds6 (0.2%)
Freezing Rain or Freezing Drizzle3 (0.1%)
↓-50.0%prior 6
Sleet; Hail3 (0.1%)
↓-72.7%prior 11
Blowing Sand; Soil; Dirt; Snow1 (0.0%)
↓-88.9%prior 9

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight2,060 (65.1%)
↓-3.2%prior 2,129
Dark - Roadway Not Lighted504 (15.9%)
↓-8.9%prior 553
Dark - Lighted Roadway343 (10.8%)
↓-23.6%prior 449
Dawn/Dusk212 (6.7%)
↑17.1%prior 181
Other/Unknown31 (1.0%)
↓-20.5%prior 39
Dark - Unknown Roadway Lighting16 (0.5%)
↓-27.3%prior 22

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry2,508 (79.2%)
↓-1.2%prior 2,538
Wet546 (17.2%)
↓-7.1%prior 588
Snow56 (1.8%)
↓-61.1%prior 144
Ice38 (1.2%)
↓-43.3%prior 67
Other/Unknown16 (0.5%)
↓-38.5%prior 26
Slush2 (0.1%)
↓-75.0%prior 8

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The types of vehicles involved in crashes remained consistent, with passenger cars, SUVs, and pickup trucks being the most common in both years. The top vehicle makes were also stable, led by Chevrolet (813 vehicles), Ford (636), and Honda (593) in 2023, though the number of Chevrolets and Fords involved decreased from 935 and 738, respectively. Analysis of persons involved shows a decrease in the 16-20 and 21-25 age groups, while the 65+ age group saw a slight increase in involvement from 837 individuals in 2022 to 869 in 2023.

Top Vehicle Makes (5,452 vehicles)

1
CHEVROLET813 (14.9%)
↓-13.0%prior 935
2
FORD636 (11.7%)
↓-13.8%prior 738
3
HONDA593 (10.9%)
↑1.0%prior 587
4
TOYOTA511 (9.4%)
↑4.5%prior 489
5
NISSAN281 (5.2%)
↑1.8%prior 276
6
HYUNDAI253 (4.6%)
↑14.0%prior 222
7
DODGE222 (4.1%)
↓-21.8%prior 284
8
JEEP204 (3.7%)
↓-10.5%prior 228
9
KIA193 (3.5%)
↓-9.4%prior 213
10
GMC150 (2.8%)
↓-3.2%prior 155

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (6,802 persons with recorded sex)

Male3,600 (52.9%)
↓-7.4%prior 3,888
Female3,202 (47.1%)
↓-2.3%prior 3,277

Source: Ohio Crash Data (ODOT TIMS) · Csv 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 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: 2023-01-01 through 2023-12-31
  • Report generated: July 6, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 3,166
  • Total persons involved: 7,062
  • Total vehicles involved: 5,452

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). "ohio, OH Crash Intelligence Report: 2023." Published July 6, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/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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Greene County, OH Crash Report — 2023 | ThatCarHitMe.com