Yearly Traffic Safety Analysis

3,679 CRASHES IN
OHIO, OH
2025

All metrics benchmarked against2024

In Clark County, total traffic crashes increased by 4.3% from 3,529 in 2024 to 3,679 in 2025. While the number of injuries decreased by 7.8% from 1,531 to 1,412, the most notable shift was a 16.7% increase in fatalities, which rose from 18 to 21 year-over-year.

3,679

4.3%was 3,529

Total Crash Events

21

16.7%was 18

Persons Killed

1,412

-7.8%was 1,531

Persons Injured

881

-4.2%was 920

Hit-and-Run Crashes

Note: "Persons Killed" (21) counts individual fatalities across all crash events. "Fatal" in the severity table below (20) 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 crash trends in Clark County show a mixed picture. The total number of crashes rose by 150 incidents, a 4.3% increase from the previous year. However, this was accompanied by a 7.8% decrease in total injuries, while total fatalities increased by 16.7% from 18 to 21.

881

Hit-and-Run Crashes — 2025

-4.2% vs prior (920)

Hit-and-run incidents showed a downward trend in Clark County. The total number of hit-and-run crashes fell from 920 in the prior year to 881 in the current year. The hit-and-run rate, or the proportion of all crashes that involved a driver leaving the scene, also decreased from 26.1% to 23.9%.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 3-33.3%

19

Motorists Killed

Prior: 1526.7%

36

Pedestrians Injured

Prior: 360.0%

1,376

Motorists Injured

Prior: 1,495-8.0%

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 remained highly consistent between the two periods. Friday was the peak day for crashes in both 2025 (565 crashes) and 2024 (620 crashes). Similarly, the 3 p.m. hour was the peak time for collisions in both years, with 270 incidents in the current period compared to 276 in the prior period.

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

While the total number of fatal crashes increased from 17 to 20, the fatal crash rate as a percentage of all crashes was unchanged at 0.5%. Crashes resulting in any form of injury (serious, minor, or possible) decreased, collectively accounting for 25.8% of incidents, down from 28.7% in the prior year. Consequently, the proportion of crashes with no reported injuries increased from 70.8% to 73.8%.

Severity is per crash event (most severe injury). 20 fatal crash events resulted in 21 persons killed.

Outcome by Severity (Crash Events)

Fatal20fatal crashes0.5%
17.6%prior 17
Serious Injury98serious injury crashes2.7%
-9.3%prior 108
Minor Injury535minor injury crashes14.5%
-7.4%prior 578
Possible Injury312possible injury crashes8.5%
-4.3%prior 326
No Injury2,714no injury crashes73.8%
8.6%prior 2,500

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 environmental conditions saw minimal changes year-over-year. Crashes in clear weather represented 53.5% of incidents, a slight decrease from 58.2% in the prior year, while crashes in cloudy conditions increased from 23.8% to 27.3%. The proportions of crashes occurring in daylight (60.5% vs. 61.2%) and on dry road surfaces (71.8% vs. 73.6%) remained stable.

Weather

Clear1,967 (53.5%)
-4.2%prior 2,054
Cloudy1,004 (27.3%)
19.4%prior 841
Rain381 (10.4%)
-5.5%prior 403
Snow229 (6.2%)
42.2%prior 161
Other/Unknown62 (1.7%)
34.8%prior 46
Fog; Smog; Smoke12 (0.3%)
-14.3%prior 14
Sleet; Hail9 (0.2%)
Blowing Sand; Soil; Dirt; Snow7 (0.2%)
Freezing Rain or Freezing Drizzle7 (0.2%)
0.0%prior 7
Severe Crosswinds1 (0.0%)

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

Lighting

Daylight2,227 (60.5%)
3.1%prior 2,161
Dark - Roadway Not Lighted580 (15.8%)
18.9%prior 488
Dark - Lighted Roadway554 (15.1%)
-5.1%prior 584
Dawn/Dusk251 (6.8%)
5.9%prior 237
Other/Unknown57 (1.5%)
42.5%prior 40
Dark - Unknown Roadway Lighting10 (0.3%)
-47.4%prior 19

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

Road Surface

Dry2,643 (71.8%)
1.8%prior 2,596
Wet666 (18.1%)
-4.4%prior 697
Snow250 (6.8%)
82.5%prior 137
Ice63 (1.7%)
21.2%prior 52
Other/Unknown47 (1.3%)
27.0%prior 37
Slush9 (0.2%)
12.5%prior 8
Water (Standing; Moving)1 (0.0%)

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 were Chevrolet (950), Ford (780), and Honda (748). While Chevrolet's numbers declined from 1,042, Ford's involvement increased from 662, moving it ahead of Honda for the second spot. The number of Sport Utility Vehicles involved in crashes grew from 1,613 to 1,775. Among persons involved, the 26-34 age group remained the largest demographic in both periods, while the number of persons in the 0-15 age group decreased from 715 to 584.

Top Vehicle Makes (6,445 vehicles)

1
CHEVROLET950 (14.7%)
-8.8%prior 1,042
2
FORD780 (12.1%)
17.8%prior 662
3
HONDA748 (11.6%)
2.5%prior 730
4
TOYOTA488 (7.6%)
5.9%prior 461
5
OTHER/UNKNOWN453 (7%)
-2.2%prior 463
6
KIA302 (4.7%)
20.3%prior 251
7
NISSAN277 (4.3%)
3.0%prior 269
8
HYUNDAI268 (4.2%)
-6.0%prior 285
9
DODGE266 (4.1%)
-22.2%prior 342
10
JEEP213 (3.3%)
-6.6%prior 228

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

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

Sex Distribution (7,317 persons with recorded sex)

Male4,253 (58.1%)
4.9%prior 4,054
Female3,064 (41.9%)
-2.4%prior 3,139

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: 3,679
  • Total persons involved: 7,974
  • Total vehicles involved: 6,445

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

ThatCarHitMe.com · An Injuria.ai Company

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