Monthly Traffic Safety Analysis

24,939 CRASHES IN
OHIO, OH
NOVEMBER 2022

All metrics benchmarked againstNovember 2021

In November 2022, there were 24,939 total crashes statewide, a 2.9% decrease from the 25,672 crashes recorded in November 2021. While overall crashes, fatalities (117 vs. 124), and injuries (8,002 vs. 8,200) saw a slight decline, motorcycle-involved crashes increased by 77.6% year-over-year, rising from 98 to 174 incidents.

24,939

-2.9%was 25,672

Total Crash Events

117

-5.6%was 124

Persons Killed

8,002

-2.4%was 8,200

Persons Injured

3,713

-9.1%was 4,086

Hit-and-Run Crashes

Note: "Persons Killed" (117) counts individual fatalities across all crash events. "Fatal" in the severity table below (108) 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 · 2022-11-01 to 2022-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Statewide traffic crash data indicates a downward trend in November 2022 compared to the same month in the prior year. Total crashes decreased by 2.9%, from 25,672 to 24,939. This trend was consistent across key metrics, with total fatalities falling by 5.6% (from 124 to 117) and total injuries declining by 2.4% (from 8,200 to 8,002).

3,713

Hit-and-Run Crashes — November 2022

-9.1% vs prior (4,086)

Hit-and-run incidents showed a downward trend in November 2022 compared to the previous year. The total number of hit-and-run crashes decreased from 4,086 to 3,713. The hit-and-run rate, which represents the proportion of all crashes that were hit-and-runs, also declined, falling from 15.9% in November 2021 to 14.9% in November 2022.

Vulnerable Road User Casualties

18

Pedestrians Killed

Prior: 24-25.0%

99

Motorists Killed

Prior: 100-1.0%

244

Pedestrians Injured

Prior: 20817.3%

7,758

Motorists Injured

Prior: 7,992-2.9%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-11-01 to 2022-11-30 · 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 a shift between November 2021 and November 2022. The peak day for collisions moved from Monday (4,577 crashes) in the prior year to Wednesday (4,383 crashes) in the current period. Similarly, the peak hour for crashes shifted from 5 p.m. in 2021 (2,303 crashes) to 6 p.m. in 2022 (2,081 crashes), indicating a change in the busiest time on the roads.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-11-01 to 2022-11-30 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-11-01 to 2022-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the severity distribution showed a slight shift towards more injurious outcomes. The fatal crash rate per 100 crashes increased marginally from 0.42% in November 2021 to 0.43% in November 2022. The proportion of serious injury crashes rose from 1.9% to 2.1% of all incidents, and minor injury crashes increased from 10.9% to 11.3%. Correspondingly, the share of crashes resulting in no injuries decreased slightly from 77.1% to 76.9%.

Severity is per crash event (most severe injury). 108 fatal crash events resulted in 117 persons killed.

Outcome by Severity (Crash Events)

Fatal108fatal crashes0.4%
-0.9%prior 109
Serious Injury512serious injury crashes2.1%
4.3%prior 491
Minor Injury2,817minor injury crashes11.3%
1.1%prior 2,786
Possible Injury2,335possible injury crashes9.4%
-6.0%prior 2,484
No Injury19,167no injury crashes76.9%
-3.2%prior 19,802

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-11-01 to 2022-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-11-01 to 2022-11-30 · Most severe injury per crash record

Road & Environmental Conditions

The environmental conditions under which crashes occurred were largely consistent between November 2021 and November 2022. In both periods, the vast majority of incidents happened in clear weather and on dry roads. The proportion of crashes on dry surfaces decreased slightly from 78.8% to 76.0% year-over-year. Similarly, crashes in clear weather conditions accounted for 59.7% of the total in the current period, down from 62.1% in the prior year, while crashes in daylight conditions saw a small proportional increase from 48.9% to 50.2%.

Weather

Clear14,902 (59.8%)
-6.5%prior 15,935
Cloudy5,337 (21.4%)
-10.2%prior 5,941
Rain2,359 (9.5%)
12.6%prior 2,095
Snow1,531 (6.1%)
25.9%prior 1,216
Fog; Smog; Smoke401 (1.6%)
464.8%prior 71
Other/Unknown237 (1.0%)
-9.9%prior 263
Sleet; Hail75 (0.3%)
-8.5%prior 82
Freezing Rain or Freezing Drizzle54 (0.2%)
1.9%prior 53
Severe Crosswinds24 (0.1%)
200.0%prior 8
Blowing Sand; Soil; Dirt; Snow19 (0.1%)
137.5%prior 8

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-11-01 to 2022-11-30 · Weather condition at time of crash

Lighting

Daylight12,512 (50.2%)
-0.3%prior 12,548
Dark - Roadway Not Lighted5,236 (21.0%)
-2.5%prior 5,371
Dark - Lighted Roadway5,071 (20.3%)
-10.0%prior 5,634
Dawn/Dusk1,764 (7.1%)
1.7%prior 1,735
Other/Unknown197 (0.8%)
-12.8%prior 226
Dark - Unknown Roadway Lighting159 (0.6%)
0.6%prior 158

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-11-01 to 2022-11-30 · Lighting condition field

Road Surface

Dry18,957 (76.0%)
-6.3%prior 20,231
Wet4,573 (18.3%)
10.6%prior 4,133
Snow706 (2.8%)
9.1%prior 647
Ice477 (1.9%)
29.6%prior 368
Other/Unknown170 (0.7%)
-7.6%prior 184
Slush36 (0.1%)
-53.8%prior 78
Water (Standing; Moving)13 (0.1%)
-18.8%prior 16
Sand; Mud; Dirt; Oil; Gravel7 (0.0%)
-53.3%prior 15

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-11-01 to 2022-11-30 · Road surface condition field

Vehicles & Demographics

The composition of vehicles and persons involved in crashes remained stable year-over-year. Chevrolet (6,327 vehicles), Ford (6,025), and Honda (3,875) were the top three vehicle makes involved in crashes in November 2022, the same as the prior year, with minor fluctuations in counts. The distribution of involved persons by age group also showed little change, with the 26-34 age bracket representing the largest cohort in both periods. The top vehicle types involved were consistently Passenger Cars, Sport Utility Vehicles, and Pick-ups.

Top Vehicle Makes (42,794 vehicles)

1
CHEVROLET6,327 (14.8%)
-5.4%prior 6,690
2
FORD6,025 (14.1%)
-3.1%prior 6,219
3
HONDA3,875 (9.1%)
2.4%prior 3,786
4
TOYOTA3,415 (8%)
2.5%prior 3,333
5
DODGE2,091 (4.9%)
-8.8%prior 2,292
6
NISSAN2,045 (4.8%)
0.8%prior 2,028
7
JEEP1,865 (4.4%)
2.6%prior 1,817
8
KIA1,785 (4.2%)
7.1%prior 1,667
9
HYUNDAI1,647 (3.8%)
-0.8%prior 1,661
10
GMC1,193 (2.8%)
-2.4%prior 1,222

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-11-01 to 2022-11-30 · Vehicle unit records

3,594 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (50,926 persons with recorded sex)

Male27,651 (54.3%)
-2.8%prior 28,440
Female23,275 (45.7%)
-3.8%prior 24,201

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-11-01 to 2022-11-30 · 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: 2022-11-01 through 2022-11-30
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2022-11-01 through 2022-11-30 (30 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 24,939
  • Total persons involved: 53,661
  • Total vehicles involved: 42,794

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: November 2022." Published July 5, 2026. Reporting period: 2022-11-01 to 2022-11-30. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/november-2022-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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