Yearly Traffic Safety Analysis

552 CRASHES IN
OHIO, OH
2025

All metrics benchmarked against2024

In Hocking County, total traffic crashes decreased by 12.2% from 629 in 2024 to 552 in 2025. This downward trend was accompanied by a significant reduction in crash severity. The most notable year-over-year change was the sharp decline in fatalities, which dropped from 7 in the prior period to 1 in the current period.

552

▼ -12.2%was 629

Total Crash Events

1

▼ -85.7%was 7

Persons Killed

181

▼ -17.4%was 219

Persons Injured

52

▼ -7.1%was 56

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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in Hocking County shows a year-over-year decrease in traffic collisions and their severity. Total crashes fell by 12.2% from 629 to 552, while total injuries declined by 17.4% from 219 to 181. The most substantial improvement was in fatalities, which decreased from 7 in 2024 to just 1 in 2025.

52

Hit-and-Run Crashes — 2025

▼ -7.1% vs prior (56)

While the absolute number of hit-and-run crashes decreased from 56 in 2024 to 52 in 2025, the hit-and-run rate showed a slight upward trend. Because the total number of crashes fell more sharply, hit-and-runs constituted a larger portion of incidents in the current period. The rate increased from 8.9% of all crashes in the prior year to 9.4% in the current year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

1

Motorists Killed

Prior: 6↓-83.3%

3

Pedestrians Injured

Prior: 5↓-40.0%

178

Motorists Injured

Prior: 214↓-16.8%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns shifted between the two periods. The day with the most crashes changed from Friday (128 incidents) in the prior year to Saturday (96 incidents) in the current year. The peak hour for collisions remained consistent, occurring in the 4 p.m. hour in both 2024 (47 crashes) and 2025 (45 crashes).

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased significantly year-over-year. The number of fatal crashes dropped from 7 to 1, with the fatal crash rate falling from 1.1% to 0.2% of all incidents. Similarly, the proportion of crashes involving a possible injury declined from 6.8% to 4.7%, while the share of crashes resulting in no injury increased from 72.8% in 2024 to 76.3% in 2025.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.2%
↓-85.7%prior 7
Serious Injury27serious injury crashes4.9%
↓-20.6%prior 34
Minor Injury77minor injury crashes13.9%
↓-11.5%prior 87
Possible Injury26possible injury crashes4.7%
↓-39.5%prior 43
No Injury421no injury crashes76.3%
↓-8.1%prior 458

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The proportion of crashes occurring on dry roads decreased from 76.6% in 2024 to 71.2% in 2025. Conversely, the share of crashes happening in snowy weather more than doubled, rising from 2.7% to 7.1% of all incidents. Crashes during daylight hours remained proportionally stable at approximately 62.7%, while those in unlighted dark conditions saw a slight proportional increase from 29.1% to 31.0%.

Weather

Clear300 (54.3%)
↓-18.9%prior 370
Cloudy137 (24.8%)
↓-12.7%prior 157
Rain56 (10.1%)
↓-18.8%prior 69
Snow39 (7.1%)
↑129.4%prior 17
Sleet; Hail6 (1.1%)
↑20.0%prior 5
Fog; Smog; Smoke6 (1.1%)
↓-14.3%prior 7
Freezing Rain or Freezing Drizzle5 (0.9%)
Other/Unknown2 (0.4%)
Severe Crosswinds1 (0.2%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight346 (62.7%)
↓-12.2%prior 394
Dark - Roadway Not Lighted171 (31.0%)
↓-6.6%prior 183
Dawn/Dusk19 (3.4%)
↓-13.6%prior 22
Dark - Lighted Roadway14 (2.5%)
↓-48.1%prior 27
Other/Unknown2 (0.4%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry393 (71.2%)
↓-18.5%prior 482
Wet98 (17.8%)
↓-12.5%prior 112
Snow42 (7.6%)
↑121.1%prior 19
Ice15 (2.7%)
↑25.0%prior 12
Slush3 (0.5%)
Sand; Mud; Dirt; Oil; Gravel1 (0.2%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes—Ford, Chevrolet, and Honda—remained the same across both periods, though the count for each decreased in 2025. An analysis of persons involved in crashes shows the age distribution remained largely consistent year-over-year. The 21-25 age group saw a slight increase in its proportional representation from 11.7% to 12.9% of all persons involved, while most other age groups experienced minimal change.

Top Vehicle Makes (742 vehicles)

1
FORD126 (17%)
↓-3.8%prior 131
2
CHEVROLET107 (14.4%)
↓-15.7%prior 127
3
HONDA94 (12.7%)
↓-5.1%prior 99
4
TOYOTA67 (9%)
↓-21.2%prior 85
5
NISSAN35 (4.7%)
↓-18.6%prior 43
6
KIA30 (4%)
→0.0%prior 30
7
JEEP28 (3.8%)
↓-41.7%prior 48
8
DODGE25 (3.4%)
↓-28.6%prior 35
9
HYUNDAI22 (3%)
↓-31.3%prior 32
10
GMC21 (2.8%)
↓-4.5%prior 22

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (985 persons with recorded sex)

Male592 (60.1%)
↓-13.3%prior 683
Female393 (39.9%)
↓-22.6%prior 508

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: July 6, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 552
  • Total persons involved: 1,015
  • Total vehicles involved: 742

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: 2025." Published July 6, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/2025-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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Hocking County, OH Crash Report — 2025 | ThatCarHitMe.com