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Yearly Traffic Safety Analysis

14,604 CRASHES IN
OHIO, OH
2022

All metrics benchmarked against2021

In 2022, Summit County recorded 14,604 total vehicle crashes, a 4.8% increase from the 13,939 crashes reported in 2021. While total collisions rose, the number of fatalities decreased from 49 to 45. A notable change was observed in pedestrian-involved incidents, where crashes increased from 108 to 126 and pedestrian fatalities more than doubled, rising from 4 in the prior year to 9 in the current period.

14,604

4.8%was 13,939

Total Crash Events

45

-8.2%was 49

Persons Killed

4,335

-1.4%was 4,397

Persons Injured

2,944

0.6%was 2,925

Hit-and-Run Crashes

Note: "Persons Killed" (45) counts individual fatalities across all crash events. "Fatal" in the severity table below (41) 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

Overall crash volume in Summit County trended upward, increasing by 4.8% from 13,939 incidents in 2021 to 14,604 in 2022. Despite the rise in total crashes, key severity metrics showed a slight decline. Total fatalities decreased from 49 to 45, and total injuries fell from 4,397 to 4,335 year-over-year.

2,944

Hit-and-Run Crashes — 2022

0.6% vs prior (2,925)

The total number of hit-and-run crashes remained relatively stable, increasing slightly from 2,925 in 2021 to 2,944 in 2022. However, due to the overall increase in total crashes during the same period, the hit-and-run rate showed a slight downward trend. Hit-and-runs constituted 20.2% of all crashes in 2022, down from 21.0% in the previous year.

Vulnerable Road User Casualties

9

Pedestrians Killed

Prior: 4125.0%

36

Motorists Killed

Prior: 45-20.0%

116

Pedestrians Injured

Prior: 10016.0%

4,219

Motorists Injured

Prior: 4,297-1.8%

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 remained largely consistent year-over-year, with Friday being the peak day for collisions in both 2022 (2,471 crashes) and 2021 (2,208 crashes). However, the daily peak hour for crashes shifted slightly earlier, moving from the 5 p.m. hour in 2021 (1,099 crashes) to the 4 p.m. hour in 2022 (1,240 crashes). Both morning and afternoon commute periods saw an increase in crash volume in the current year.

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

While total crashes increased, the overall severity of crashes saw a slight decrease between the two periods. The number of fatal crashes fell from 43 to 41, and total fatalities dropped from 49 to 45. The proportion of crashes resulting in serious injuries also declined, from 1.7% in 2021 to 1.5% in 2022. Correspondingly, crashes with no reported injuries accounted for a slightly larger share of the total, rising from 77.8% to 78.4%.

Severity is per crash event (most severe injury). 41 fatal crash events resulted in 45 persons killed.

Outcome by Severity (Crash Events)

Fatal41fatal crashes0.3%
-4.7%prior 43
Serious Injury220serious injury crashes1.5%
-8.3%prior 240
Minor Injury1,489minor injury crashes10.2%
5.5%prior 1,411
Possible Injury1,403possible injury crashes9.6%
-0.1%prior 1,405
No Injury11,451no injury crashes78.4%
5.6%prior 10,840

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 across lighting conditions remained stable year-over-year, with most incidents occurring in daylight. Regarding road conditions, a notable shift was observed in crashes occurring on snowy roads, which increased from 366 in 2021 to 826 in 2022. This corresponds with a rise in crashes during snow weather conditions, which grew from 624 to 963 incidents, representing 6.6% of all crashes in 2022 compared to 4.5% in the prior year.

Weather

Clear8,225 (56.3%)
3.2%prior 7,972
Cloudy3,774 (25.8%)
3.9%prior 3,634
Rain1,332 (9.1%)
-6.6%prior 1,426
Snow963 (6.6%)
54.3%prior 624
Other/Unknown196 (1.3%)
-8.4%prior 214
Fog; Smog; Smoke48 (0.3%)
77.8%prior 27
Freezing Rain or Freezing Drizzle25 (0.2%)
108.3%prior 12
Sleet; Hail20 (0.1%)
5.3%prior 19
Blowing Sand; Soil; Dirt; Snow14 (0.1%)
Severe Crosswinds7 (0.0%)
-12.5%prior 8

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

Lighting

Daylight9,914 (67.9%)
6.4%prior 9,322
Dark - Lighted Roadway2,913 (19.9%)
0.2%prior 2,908
Dark - Roadway Not Lighted810 (5.5%)
2.5%prior 790
Dawn/Dusk758 (5.2%)
13.0%prior 671
Other/Unknown137 (0.9%)
-19.4%prior 170
Dark - Unknown Roadway Lighting72 (0.5%)
-7.7%prior 78

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

Road Surface

Dry10,720 (73.4%)
0.8%prior 10,633
Wet2,678 (18.3%)
3.3%prior 2,593
Snow826 (5.7%)
125.7%prior 366
Other/Unknown171 (1.2%)
31.5%prior 130
Ice162 (1.1%)
-16.9%prior 195
Slush39 (0.3%)
143.8%prior 16
Water (Standing; Moving)6 (0.0%)
Sand; Mud; Dirt; Oil; Gravel2 (0.0%)

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

Vehicles & Demographics

The top five vehicle makes involved in crashes—Ford, Chevrolet, Toyota, Honda, and Dodge—remained the same across both periods, with no significant changes in their rankings. A shift was observed in vehicle types, as crashes involving Sport Utility Vehicles increased from 6,316 to 7,305, while those involving Passenger Cars decreased from 12,346 to 12,112. The age distribution of persons involved in crashes showed no major changes, with the 26-34 age group representing the largest cohort in both 2021 and 2022.

Top Vehicle Makes (27,261 vehicles)

1
FORD3,688 (13.5%)
-0.6%prior 3,712
2
CHEVROLET3,597 (13.2%)
5.2%prior 3,418
3
TOYOTA2,067 (7.6%)
6.2%prior 1,947
4
HONDA2,056 (7.5%)
0.5%prior 2,046
5
DODGE1,472 (5.4%)
13.2%prior 1,300
6
JEEP1,304 (4.8%)
12.6%prior 1,158
7
HYUNDAI1,304 (4.8%)
2.3%prior 1,275
8
NISSAN1,288 (4.7%)
7.7%prior 1,196
9
KIA1,285 (4.7%)
13.7%prior 1,130
10
GMC666 (2.4%)
12.3%prior 593

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

2,815 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (31,385 persons with recorded sex)

Male16,821 (53.6%)
5.2%prior 15,984
Female14,564 (46.4%)
4.6%prior 13,925

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: August 22, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 14,604
  • Total persons involved: 33,975
  • Total vehicles involved: 27,261

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 August 22, 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

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