Yearly Traffic Safety Analysis

24,205 CRASHES IN
OHIO, OH
2025

All metrics benchmarked against2024

In 2025, Cuyahoga County recorded 24,205 total crashes, an 8.2% increase from the 22,370 crashes documented in 2024. Despite the overall rise in collisions, the most significant year-over-year shift was a 27.9% decrease in traffic fatalities, which fell from 118 in 2024 to 85 in 2025.

24,205

▲ 8.2%was 22,370

Total Crash Events

▼ -28.0%was 118

Persons Killed

9,555

▲ 2.6%was 9,311

Persons Injured

4,499

▲ 7.8%was 4,174

Hit-and-Run Crashes

Note: "Persons Killed" (85) counts individual fatalities across all crash events. "Fatal" in the severity table below (78) 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 shows an increase in the total number of crashes, which rose by 1,835 incidents (8.2%) from 2024 to 2025. This was accompanied by a modest 2.6% increase in total injuries, from 9,311 to 9,555. In contrast, total fatalities showed a strong downward trend, decreasing by 27.9% year-over-year.

4,499

Hit-and-Run Crashes — 2025

▲ 7.8% vs prior (4,174)

The absolute number of hit-and-run crashes increased from 4,174 in 2024 to 4,499 in 2025. However, due to the overall increase in total crashes, the hit-and-run rate as a percentage of all incidents remained stable. The rate saw a negligible decrease from 18.7% in the prior year to 18.6% in the current year.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 16↑12.5%

Motorists Killed

Prior: 102↓-34.3%

351

Pedestrians Injured

Prior: 369↓-4.9%

9,204

Motorists Injured

Prior: 8,942↑2.9%

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 shifted slightly year-over-year. The peak hour for collisions remained 3 p.m. in both periods, though the crash volume in that hour increased from 1,799 to 1,954. The most crash-prone day of the week moved from Friday in 2024 (3,732 crashes) to Thursday in 2025 (3,916 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 showed a notable improvement in 2025. The proportion of crashes that were fatal decreased from 0.5% in 2024 to 0.3% in 2025. Concurrently, the share of crashes resulting in any level of injury (Serious, Minor, or Possible) declined from 28.7% to 27.6%, while the proportion of non-injury crashes rose from 70.8% to 72.2%.

Severity is per crash event (most severe injury). 78 fatal crash events resulted in 85 persons killed.

Outcome by Severity (Crash Events)

Fatal78fatal crashes0.3%

↓-29.7%prior 111

Serious Injury643serious injury crashes2.7%

→0.0%prior 643

Minor Injury2,682minor injury crashes11.1%

↑6.1%prior 2,527

Possible Injury3,338possible injury crashes13.8%

↑2.7%prior 3,250

No Injury17,464no injury crashes72.2%

↑10.3%prior 15,839

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

Crash conditions in 2025 were marked by a significant increase in winter-weather incidents compared to 2024. The number of crashes occurring in snow conditions grew from 1,437 to 2,557, and the proportion of crashes on snowy or icy roads rose from 6.4% to 11.1% of all crashes. Conversely, the share of crashes on dry roads fell from 72.6% to 68.3%. The distribution of crashes by lighting conditions remained proportionally stable between the two years.

Weather

Clear14,100 (58.3%)

↑4.8%prior 13,455

Cloudy4,786 (19.8%)

↑8.2%prior 4,422

Snow2,557 (10.6%)

↑77.9%prior 1,437

Rain2,272 (9.4%)

↓-14.0%prior 2,641

Other/Unknown279 (1.2%)

↑40.9%prior 198

Sleet; Hail88 (0.4%)

↑49.2%prior 59

Freezing Rain or Freezing Drizzle69 (0.3%)

↑13.1%prior 61

Fog; Smog; Smoke30 (0.1%)

↓-57.1%prior 70

Severe Crosswinds13 (0.1%)

↑30.0%prior 10

Blowing Sand; Soil; Dirt; Snow11 (0.0%)

↓-35.3%prior 17

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

Lighting

Daylight15,895 (65.7%)

↑7.8%prior 14,750

Dark - Lighted Roadway6,001 (24.8%)

↑5.7%prior 5,677

Dawn/Dusk1,385 (5.7%)

↑19.1%prior 1,163

Dark - Roadway Not Lighted577 (2.4%)

↑11.0%prior 520

Other/Unknown214 (0.9%)

↑30.5%prior 164

Dark - Unknown Roadway Lighting133 (0.5%)

↑38.5%prior 96

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

Road Surface

Dry16,522 (68.3%)

↑1.8%prior 16,231

Wet4,624 (19.1%)

↑3.9%prior 4,450

Snow2,151 (8.9%)

↑104.1%prior 1,054

Ice540 (2.2%)

↑41.0%prior 383

Other/Unknown207 (0.9%)

↑44.8%prior 143

Slush116 (0.5%)

↑81.3%prior 64

Water (Standing; Moving)39 (0.2%)

↑2.6%prior 38

Sand; Mud; Dirt; Oil; Gravel6 (0.0%)

↓-14.3%prior 7

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

Vehicles & Demographics

The types of vehicles and demographics of persons involved in crashes remained largely consistent year-over-year. Ford and Chevrolet were the top identified vehicle makes in both periods, following 'OTHER/UNKNOWN'. The age distribution of all persons involved in crashes also showed minimal change, with the proportional representation for every reported age group shifting by less than one percentage point between 2024 and 2025.

Top Vehicle Makes (46,296 vehicles)

OTHER/UNKNOWN7,164 (15.5%)

↑1.7%prior 7,046

FORD4,886 (10.6%)

↑8.7%prior 4,495

CHEVROLET4,866 (10.5%)

↑7.7%prior 4,520

TOYOTA3,536 (7.6%)

↑7.1%prior 3,302

HONDA3,323 (7.2%)

↑13.3%prior 2,933

NISSAN2,226 (4.8%)

↑2.9%prior 2,164

JEEP2,222 (4.8%)

↑9.0%prior 2,038

KIA1,964 (4.2%)

↑16.6%prior 1,684

HYUNDAI1,958 (4.2%)

↑10.6%prior 1,770

DODGE1,235 (2.7%)

↓-10.7%prior 1,383

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

4,157 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (53,673 persons with recorded sex)

Male29,156 (54.3%)

↑7.9%prior 27,014

Female24,517 (45.7%)

↑7.1%prior 22,896

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: 24,205
Total persons involved: 57,216
Total vehicles involved: 46,296

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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24,205 CRASHES INOHIO, OH2025