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Yearly Traffic Safety Analysis

12,683 CRASHES IN
OHIO, OH
2024

All metrics benchmarked against2023

In Summit County, total traffic crashes decreased by 1.9% from 12,926 in 2023 to 12,683 in 2024. During this same period, total fatalities saw a more significant decline, dropping 19.6% from 46 to 37. The most notable shift was a 50% reduction in pedestrian fatalities, which fell from 12 in the prior year to 6 in the current year.

12,683

▼ -1.9%was 12,926

Total Crash Events

▼ -19.6%was 46

Persons Killed

3,886

▼ -0.7%was 3,914

Persons Injured

2,311

▼ -8.0%was 2,512

Hit-and-Run Crashes

Note: "Persons Killed" (37) counts individual fatalities across all crash events. "Fatal" in the severity table below (37) 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 · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends in Summit County showed a slight improvement year-over-year. Total crashes decreased by 1.9%, from 12,926 to 12,683. Similarly, total injuries saw a minor reduction of 0.7% (from 3,914 to 3,886), while fatalities decreased more substantially by 19.6% (from 46 to 37).

2,311

Hit-and-Run Crashes — 2024

▼ -8.0% vs prior (2,512)

The number of hit-and-run incidents decreased from 2,512 in 2023 to 2,311 in 2024. This decline was also reflected in the rate, which fell from 19.4% of all crashes in the prior period to 18.2% in the current period. This represents a downward trend in both the absolute count and the proportion of hit-and-run crashes.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 12↓-50.0%

Motorists Killed

Prior: 34↓-8.8%

103

Pedestrians Injured

Prior: 140↓-26.4%

3,783

Motorists Injured

Prior: 3,774↑0.2%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of peak crash activity shifted slightly between the two periods. In 2024, the peak day for crashes was Thursday (2,124 crashes), a change from Friday (2,090 crashes) in the prior year. The peak hour also moved later, from the 4 PM hour in 2023 (1,163 crashes) to the 5 PM hour in 2024 (1,118 crashes). Midday and evening commute hours on weekdays remain the highest-risk times in both periods.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes showed a positive trend, with the fatal crash rate decreasing from 0.35% of all incidents in 2023 to 0.29% in 2024. The proportion of crashes resulting in serious injuries (1.9% vs 2.0%) and minor injuries (10.1% vs 10.3%) also saw slight decreases. Conversely, crashes categorized with 'possible injury' increased slightly as a share of the total, from 9.1% to 9.7%.

Outcome by Severity (Crash Events)

Fatal37fatal crashes0.3%

↓-17.8%prior 45

Serious Injury238serious injury crashes1.9%

↓-6.3%prior 254

Minor Injury1,287minor injury crashes10.1%

↓-3.0%prior 1,327

Possible Injury1,227possible injury crashes9.7%

↑4.1%prior 1,179

No Injury9,894no injury crashes78%

↓-2.2%prior 10,121

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The distribution of crashes across various environmental conditions remained highly consistent year-over-year. In both 2023 and 2024, crashes occurred most frequently in clear weather (55.2% vs 58.2%), during daylight hours (67.4% vs 67.6%), and on dry road surfaces (73.8% vs 74.6%). There were no significant shifts in the proportion of crashes occurring under adverse weather, lighting, or road conditions between the two periods.

Weather

Clear7,383 (58.2%)

↑3.4%prior 7,138

Cloudy2,970 (23.4%)

↓-11.9%prior 3,373

Rain1,543 (12.2%)

↓-0.1%prior 1,545

Snow575 (4.5%)

↓-7.7%prior 623

Other/Unknown129 (1.0%)

↓-19.9%prior 161

Freezing Rain or Freezing Drizzle30 (0.2%)

↑150.0%prior 12

Fog; Smog; Smoke25 (0.2%)

↓-52.8%prior 53

Sleet; Hail18 (0.1%)

↑63.6%prior 11

Blowing Sand; Soil; Dirt; Snow5 (0.0%)

Severe Crosswinds5 (0.0%)

↓-37.5%prior 8

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight8,571 (67.6%)

↓-1.6%prior 8,713

Dark - Lighted Roadway2,450 (19.3%)

↓-2.8%prior 2,520

Dark - Roadway Not Lighted776 (6.1%)

↑8.1%prior 718

Dawn/Dusk706 (5.6%)

↓-9.8%prior 783

Other/Unknown109 (0.9%)

↓-2.7%prior 112

Dark - Unknown Roadway Lighting71 (0.6%)

↓-11.3%prior 80

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry9,456 (74.6%)

↓-0.9%prior 9,539

Wet2,581 (20.4%)

↓-4.1%prior 2,690

Snow398 (3.1%)

↓-4.8%prior 418

Ice114 (0.9%)

↓-0.9%prior 115

Other/Unknown112 (0.9%)

↓-18.2%prior 137

Slush11 (0.1%)

↓-21.4%prior 14

Water (Standing; Moving)8 (0.1%)

↓-33.3%prior 12

Sand; Mud; Dirt; Oil; Gravel3 (0.0%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes showed stability, with Ford, Chevrolet, Honda, Toyota, and Kia being the top five most common makes in both 2023 and 2024, in the same order. The age distribution of persons involved in crashes was also consistent, with the 26-34 age group representing the largest cohort in both years (15.6% vs 15.9%). There was a minor proportional increase in the 65+ age group, which grew from 11.1% of persons involved to 11.8%.

Top Vehicle Makes (23,742 vehicles)

FORD3,129 (13.2%)

↓-4.0%prior 3,261

CHEVROLET2,871 (12.1%)

↓-7.6%prior 3,107

HONDA1,916 (8.1%)

↑1.8%prior 1,883

TOYOTA1,853 (7.8%)

↑2.0%prior 1,817

KIA1,321 (5.6%)

↑2.5%prior 1,289

JEEP1,174 (4.9%)

↓-0.4%prior 1,179

NISSAN1,123 (4.7%)

↑0.2%prior 1,121

HYUNDAI1,120 (4.7%)

↓-6.4%prior 1,197

DODGE999 (4.2%)

↓-10.3%prior 1,114

OTHER/UNKNOWN661 (2.8%)

↓-9.6%prior 731

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

2,244 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (27,888 persons with recorded sex)

Male14,909 (53.5%)

↓-2.5%prior 15,284

Female12,979 (46.5%)

↓-1.6%prior 13,187

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
Report generated: August 22, 2026

Data Coverage

Reporting period: 2024-01-01 through 2024-12-31 (366 days)
Geographic scope: ohio, OH
Total crash records analyzed: 12,683
Total persons involved: 29,959
Total vehicles involved: 23,742

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: 2024." Published August 22, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/2024-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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