Yearly Traffic Safety Analysis

960 CRASHES IN
OHIO, OH
2025

All metrics benchmarked against2024

In Jefferson County, total traffic crashes remained stable, with 960 incidents in 2025 compared to 961 in 2024. While the overall crash volume was nearly unchanged, the most notable year-over-year shift was a significant reduction in crash severity. Fatalities saw a substantial decrease, dropping from 8 in the prior year to 2 in the current year.

960

-0.1%was 961

Total Crash Events

2

-75.0%was 8

Persons Killed

326

-12.4%was 372

Persons Injured

61

-28.2%was 85

Hit-and-Run Crashes

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

While the total number of crashes in Jefferson County was virtually unchanged year-over-year, decreasing by a single incident, the severity of these crashes has trended downward. Total injuries fell by 12.4% from 372 to 326. Most significantly, traffic fatalities decreased by 75%, from 8 deaths in 2024 to 2 in 2025.

61

Hit-and-Run Crashes — 2025

-28.2% vs prior (85)

Hit-and-run incidents saw a significant decline in the current period. The total number of hit-and-run crashes fell by 28.2%, from 85 in 2024 to 61 in 2025. As a percentage of all collisions, the hit-and-run rate decreased from 8.8% to 6.4%.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

2

Motorists Killed

Prior: 8-75.0%

2

Pedestrians Injured

Prior: 3-33.3%

324

Motorists Injured

Prior: 369-12.2%

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 peak days for crashes remained mid-week, shifting from Tuesday in 2024 (164 crashes) to a tie between Tuesday and Thursday in 2025 (159 crashes each). The evening commute hour became more pronounced as a peak time for collisions. The peak crash hour shifted from 3 p.m. in the prior period (69 crashes) to 5 p.m. in the current period (80 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 outcomes improved significantly compared to the prior year. The number of fatal crashes fell from 7 to 2, and serious injury crashes decreased from 41 to 28. This shift is reflected in the proportion of non-injury crashes, which rose from 71.9% of all incidents in 2024 to 75.8% in 2025.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.2%
-71.4%prior 7
Serious Injury28serious injury crashes2.9%
-31.7%prior 41
Minor Injury117minor injury crashes12.2%
-19.3%prior 145
Possible Injury85possible injury crashes8.9%
10.4%prior 77
No Injury728no injury crashes75.8%
5.4%prior 691

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 by lighting conditions remained consistent year-over-year, with daylight hours accounting for the majority of incidents in both periods. However, there was a notable increase in crashes occurring during adverse weather, with incidents in snow rising from 43 to 79. Correspondingly, crashes on snowy road surfaces more than doubled from 33 to 68, while crashes on dry roads decreased from 711 to 661.

Weather

Clear541 (56.4%)
-6.7%prior 580
Cloudy204 (21.3%)
2.5%prior 199
Rain117 (12.2%)
-6.4%prior 125
Snow79 (8.2%)
83.7%prior 43
Fog; Smog; Smoke8 (0.8%)
-11.1%prior 9
Sleet; Hail5 (0.5%)
Other/Unknown2 (0.2%)
Freezing Rain or Freezing Drizzle2 (0.2%)
Severe Crosswinds1 (0.1%)
Blowing Sand; Soil; Dirt; Snow1 (0.1%)

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

Lighting

Daylight645 (67.2%)
-1.8%prior 657
Dark - Roadway Not Lighted161 (16.8%)
-6.9%prior 173
Dark - Lighted Roadway92 (9.6%)
13.6%prior 81
Dawn/Dusk59 (6.1%)
28.3%prior 46
Dark - Unknown Roadway Lighting2 (0.2%)
Other/Unknown1 (0.1%)

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

Road Surface

Dry661 (68.9%)
-7.0%prior 711
Wet212 (22.1%)
5.0%prior 202
Snow68 (7.1%)
106.1%prior 33
Ice11 (1.1%)
10.0%prior 10
Slush7 (0.7%)
Other/Unknown1 (0.1%)

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

Vehicles & Demographics

The primary vehicles involved in crashes remained consistent, with Passenger Cars, SUVs, and Pick-ups comprising the top three in both years. Among vehicle makes, Ford and Chevrolet continued to be the most frequently involved, though Toyota's involvement increased from 121 to 140 vehicles, surpassing Honda for the third position. The 26-34 age group became the most represented demographic involved in crashes, overtaking the 35-44 age group from the prior year.

Top Vehicle Makes (1,483 vehicles)

1
FORD239 (16.1%)
-7.4%prior 258
2
CHEVROLET205 (13.8%)
7.3%prior 191
3
TOYOTA140 (9.4%)
15.7%prior 121
4
HONDA135 (9.1%)
-2.9%prior 139
5
JEEP74 (5%)
-9.8%prior 82
6
DODGE65 (4.4%)
-11.0%prior 73
7
KIA64 (4.3%)
14.3%prior 56
8
NISSAN53 (3.6%)
-3.6%prior 55
9
HYUNDAI51 (3.4%)
-26.1%prior 69
10
GMC47 (3.2%)
-6.0%prior 50

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

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

Sex Distribution (1,900 persons with recorded sex)

Male1,100 (57.9%)
3.8%prior 1,060
Female800 (42.1%)
-4.8%prior 840

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: 960
  • Total persons involved: 1,936
  • Total vehicles involved: 1,483

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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Jefferson County, OH Crash Report — 2025 | ThatCarHitMe.com