Yearly Traffic Safety Analysis

586 CRASHES IN
OHIO, OH
2025

All metrics benchmarked against2024

In 2025, Coshocton County recorded 586 total crashes, an 8.1% increase from the 542 crashes reported in 2024. While total fatalities and injuries saw slight decreases, the most significant year-over-year change was in hit-and-run incidents, which increased from 1 to 10.

586

8.1%was 542

Total Crash Events

5

-16.7%was 6

Persons Killed

191

-4.5%was 200

Persons Injured

10

900.0%was 1

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

Trend Summary

Overall, total crashes in Coshocton County increased by 8.1% from 2024 to 2025, rising from 542 to 586 incidents. Despite the rise in crash volume, the number of resulting injuries decreased by 4.5% (from 200 to 191), and fatalities fell from 6 to 5.

10

Hit-and-Run Crashes — 2025

900.0% vs prior (1)

Hit-and-run crashes saw a significant increase in 2025 compared to the prior year. The number of incidents rose from just 1 in 2024 to 10 in 2025. Consequently, the hit-and-run rate, which represents the percentage of total crashes that were hit-and-runs, increased from 0.2% to 1.7%.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

5

Motorists Killed

Prior: 6-16.7%

2

Pedestrians Injured

Prior: 20.0%

189

Motorists Injured

Prior: 198-4.5%

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

The temporal patterns of crashes showed a shift between the two periods. In 2025, the peak day for crashes was Tuesday with 94 incidents, a change from Saturday (92 incidents) in the prior year. The peak hour for crashes also shifted later, from the 5 p.m. hour in 2024 to the 6 p.m. hour in 2025, which saw 50 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

The severity of crashes decreased from 2024 to 2025. The fatal crash rate fell from 1.11% to 0.85%, with 5 fatal crashes in 2025 compared to 6 in the prior year. The proportion of crashes resulting in serious injuries also declined from 4.6% to 3.1%. Conversely, the share of crashes with no reported injuries increased from 73.1% to 76.1%.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.9%
-16.7%prior 6
Serious Injury18serious injury crashes3.1%
-28.0%prior 25
Minor Injury71minor injury crashes12.1%
-1.4%prior 72
Possible Injury46possible injury crashes7.8%
7.0%prior 43
No Injury446no injury crashes76.1%
12.6%prior 396

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 distribution of crashes across environmental conditions remained largely stable, with most incidents in both years occurring in daylight (58.4% in 2025 vs. 56.6% in 2024) and on dry roads (74.9% vs. 75.3%). However, there was a notable shift in weather-related crashes; incidents during snowy conditions doubled from 18 to 36. Conversely, crashes occurring in rain decreased from 58 to 37.

Weather

Clear339 (57.8%)
-4.5%prior 355
Cloudy153 (26.1%)
64.5%prior 93
Rain37 (6.3%)
-36.2%prior 58
Snow36 (6.1%)
100.0%prior 18
Fog; Smog; Smoke10 (1.7%)
-16.7%prior 12
Freezing Rain or Freezing Drizzle7 (1.2%)
40.0%prior 5
Sleet; Hail2 (0.3%)
Other/Unknown1 (0.2%)
Blowing Sand; Soil; Dirt; Snow1 (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

Daylight342 (58.4%)
11.4%prior 307
Dark - Roadway Not Lighted164 (28.0%)
17.1%prior 140
Dawn/Dusk39 (6.7%)
-17.0%prior 47
Dark - Lighted Roadway36 (6.1%)
-7.7%prior 39
Dark - Unknown Roadway Lighting4 (0.7%)
-50.0%prior 8
Other/Unknown1 (0.2%)

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

Road Surface

Dry439 (74.9%)
7.6%prior 408
Wet80 (13.7%)
-25.2%prior 107
Snow41 (7.0%)
141.2%prior 17
Ice19 (3.2%)
Sand; Mud; Dirt; Oil; Gravel3 (0.5%)
Slush3 (0.5%)
Other/Unknown1 (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 vehicle makes involved in crashes saw a slight shuffle, with Ford (147 vehicles) surpassing Chevrolet (145 vehicles) for the most common make in 2025, reversing the order from the previous year. The distribution of vehicle types remained consistent, with passenger cars, SUVs, and pickups being the most common in both periods. A notable demographic shift occurred in the age of persons involved in crashes, with a 29% increase in individuals aged 65 and older, from 148 in 2024 to 191 in 2025.

Top Vehicle Makes (879 vehicles)

1
FORD147 (16.7%)
6.5%prior 138
2
CHEVROLET145 (16.5%)
-6.5%prior 155
3
HONDA83 (9.4%)
-4.6%prior 87
4
DODGE60 (6.8%)
7.1%prior 56
5
TOYOTA58 (6.6%)
1.8%prior 57
6
GMC55 (6.3%)
150.0%prior 22
7
JEEP48 (5.5%)
2.1%prior 47
8
KIA35 (4%)
59.1%prior 22
9
NISSAN27 (3.1%)
-12.9%prior 31
10
SUBARU26 (3%)
73.3%prior 15

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

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

Sex Distribution (1,117 persons with recorded sex)

Male652 (58.4%)
8.1%prior 603
Female465 (41.6%)
3.1%prior 451

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 5, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 586
  • Total persons involved: 1,123
  • Total vehicles involved: 879

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 5, 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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Coshocton County, OH Crash Report — 2025 | ThatCarHitMe.com