Yearly Traffic Safety Analysis

30,904 CRASHES IN
OHIO, OH
2022

All metrics benchmarked against2021

In Cuyahoga County, the total number of traffic crashes remained stable, with 30,904 incidents in 2022 compared to 30,809 in 2021, an increase of just 0.3%. Despite the consistent crash volume, the most notable year-over-year shift was a significant 33.1% decrease in fatalities, which fell from 130 to 87. This reduction in severe outcomes occurred alongside a 5.3% drop in total injuries.

30,904

0.3%was 30,809

Total Crash Events

87

-33.1%was 130

Persons Killed

12,647

-5.3%was 13,350

Persons Injured

7,364

-6.6%was 7,886

Hit-and-Run Crashes

Note: "Persons Killed" (87) counts individual fatalities across all crash events. "Fatal" in the severity table below (83) 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 · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in crash volume was stable, with an increase of only 95 incidents from 30,809 in 2021 to 30,904 in 2022. However, the severity of these crashes decreased, as total injuries fell by 5.3% from 13,350 to 12,647. Most significantly, total fatalities dropped by 33.1%, from 130 in the prior year to 87 in the current year.

7,364

Hit-and-Run Crashes — 2022

-6.6% vs prior (7,886)

The number of hit-and-run crashes decreased from 7,886 in 2021 to 7,364 in 2022, a reduction of 522 incidents. This decline was also reflected in the hit-and-run rate, which represents the proportion of all crashes classified as such. The rate fell from 25.6% of all crashes in the prior period to 23.8% in the current period, indicating a downward trend.

Vulnerable Road User Casualties

21

Pedestrians Killed

Prior: 23-8.7%

66

Motorists Killed

Prior: 107-38.3%

395

Pedestrians Injured

Prior: 34514.5%

12,252

Motorists Injured

Prior: 13,005-5.8%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · 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 remarkable consistency year-over-year. Friday remained the peak day for crashes in both 2022 (5,182 crashes) and 2021 (5,167 crashes). Similarly, the 4 p.m. hour was the busiest time for collisions in both periods, with 2,623 incidents in 2022 and 2,617 in 2021, indicating no significant shift in daily or weekly crash timing.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased in 2022 compared to the prior year. The number of fatal crashes fell from 122 to 83, and the proportion of all crashes that were fatal declined from 0.4% to 0.3%. The combined share of crashes resulting in any level of injury (serious, minor, or possible) also decreased from 29.0% in 2021 to 28.3% in 2022. Consequently, the proportion of crashes involving no injuries rose from 70.6% to 71.7%.

Severity is per crash event (most severe injury). 83 fatal crash events resulted in 87 persons killed.

Outcome by Severity (Crash Events)

Fatal83fatal crashes0.3%
-32.0%prior 122
Serious Injury710serious injury crashes2.3%
-5.8%prior 754
Minor Injury2,993minor injury crashes9.7%
-4.6%prior 3,137
Possible Injury4,959possible injury crashes16%
-1.9%prior 5,055
No Injury22,159no injury crashes71.7%
1.9%prior 21,741

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The distribution of crashes by lighting condition was nearly identical year-over-year, with approximately 66% occurring in daylight. While most crashes in both periods occurred on dry roads in clear weather, there was a notable shift in adverse condition crashes. The number of crashes on snowy roads more than doubled, increasing from 934 in 2021 to 1,956 in 2022, raising their share of total crashes from 3.0% to 6.3%.

Weather

Clear18,000 (58.2%)
-3.8%prior 18,704
Cloudy7,011 (22.7%)
-1.9%prior 7,147
Rain2,774 (9.0%)
-5.8%prior 2,946
Snow2,213 (7.2%)
77.3%prior 1,248
Other/Unknown569 (1.8%)
-9.7%prior 630
Sleet; Hail129 (0.4%)
115.0%prior 60
Freezing Rain or Freezing Drizzle93 (0.3%)
121.4%prior 42
Fog; Smog; Smoke68 (0.2%)
277.8%prior 18
Blowing Sand; Soil; Dirt; Snow33 (0.1%)
Severe Crosswinds14 (0.0%)
16.7%prior 12

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight20,430 (66.1%)
1.3%prior 20,159
Dark - Lighted Roadway7,783 (25.2%)
-2.8%prior 8,004
Dawn/Dusk1,589 (5.1%)
2.8%prior 1,545
Dark - Roadway Not Lighted561 (1.8%)
5.3%prior 533
Other/Unknown358 (1.2%)
-11.2%prior 403
Dark - Unknown Roadway Lighting183 (0.6%)
10.9%prior 165

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry21,869 (70.8%)
-6.8%prior 23,461
Wet5,767 (18.7%)
4.8%prior 5,501
Snow1,956 (6.3%)
109.4%prior 934
Ice681 (2.2%)
152.2%prior 270
Other/Unknown439 (1.4%)
-13.1%prior 505
Slush130 (0.4%)
217.1%prior 41
Water (Standing; Moving)52 (0.2%)
-40.2%prior 87
Sand; Mud; Dirt; Oil; Gravel10 (0.0%)
0.0%prior 10

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes, Chevrolet and Ford, remained consistent between the two periods, though both saw a minor reduction in counts. The number of Sport Utility Vehicles in crashes increased from 13,167 to 14,286, while Passenger Cars decreased slightly. Among persons involved, the 26-34 age group was the largest in both years, while the 65+ age group saw its involvement grow from 6,305 individuals to 6,930.

Top Vehicle Makes (59,589 vehicles)

1
CHEVROLET6,810 (11.4%)
-4.5%prior 7,129
2
FORD6,740 (11.3%)
-2.7%prior 6,924
3
OTHER/UNKNOWN5,958 (10%)
5.0%prior 5,675
4
TOYOTA4,406 (7.4%)
4.0%prior 4,238
5
HONDA4,066 (6.8%)
2.6%prior 3,963
6
NISSAN3,103 (5.2%)
-0.5%prior 3,119
7
KIA2,654 (4.5%)
4.6%prior 2,538
8
JEEP2,653 (4.5%)
11.6%prior 2,377
9
HYUNDAI2,628 (4.4%)
1.0%prior 2,602
10
DODGE2,271 (3.8%)
0.6%prior 2,257

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

8,166 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (66,497 persons with recorded sex)

Male35,579 (53.5%)
1.3%prior 35,134
Female30,918 (46.5%)
0.2%prior 30,846

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 30,904
  • Total persons involved: 72,569
  • Total vehicles involved: 59,589

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: 2022." Published July 5, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/2022-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

Cuyahoga County, OH Crash Report — 2022 | ThatCarHitMe.com