Yearly Traffic Safety Analysis

2,146 CRASHES IN
OHIO, OH
2022

All metrics benchmarked against2021

In Hancock County, traffic crashes increased slightly from 2,103 in the prior period to 2,146 in the current period, a change of approximately 2.0%. While total crashes and injuries (544, up from 520) saw modest increases, the most significant year-over-year shift was a 40% decrease in traffic-related fatalities, which fell from 10 to 6.

2,146

2.0%was 2,103

Total Crash Events

6

-40.0%was 10

Persons Killed

544

4.6%was 520

Persons Injured

219

-0.9%was 221

Hit-and-Run Crashes

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) 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 shows a slight increase in traffic incidents year-over-year. Total crashes rose by 43, from 2,103 to 2,146 (+2.0%), and the number of people injured increased from 520 to 544 (+4.6%). In contrast, the number of fatalities saw a substantial decrease, dropping from 10 in the prior year to 6 in the current year.

219

Hit-and-Run Crashes — 2022

-0.9% vs prior (221)

The frequency of hit-and-run incidents remained relatively stable year-over-year. The total number of hit-and-run crashes decreased slightly from 221 to 219. As a percentage of all crashes, the hit-and-run rate saw a minor decrease from 10.5% in the prior period to 10.2% in the current period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2-50.0%

5

Motorists Killed

Prior: 8-37.5%

4

Pedestrians Injured

Prior: 13-69.2%

540

Motorists Injured

Prior: 5076.5%

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 consistent year-over-year. The peak day for crashes in both periods was Friday, with 393 incidents in the current period and 385 in the prior. Similarly, the 5 p.m. hour was the peak hour for crashes in both years, recording 147 crashes in the current period and 176 in the prior period. The general pattern of more crashes occurring during the evening commute has not changed.

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 severity of those crashes lessened year-over-year. The number of fatal crashes decreased from 10 to 6, and the corresponding fatality rate dropped from 0.48% to 0.28% of all crashes. Conversely, crashes involving serious injuries increased from 30 to 36, representing a rise from 1.4% to 1.7% of total incidents. The proportion of crashes with no injuries remained stable at approximately 82% for both periods.

Outcome by Severity (Crash Events)

Fatal6fatal crashes0.3%
-40.0%prior 10
Serious Injury36serious injury crashes1.7%
20.0%prior 30
Minor Injury208minor injury crashes9.7%
4.0%prior 200
Possible Injury139possible injury crashes6.5%
3.0%prior 135
No Injury1,757no injury crashes81.9%
1.7%prior 1,728

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

Driving conditions for crashes were largely similar between the two periods. Crashes in clear weather and on dry roads accounted for the majority of incidents in both years, with proportions remaining stable. There was a slight increase in crashes occurring in adverse winter conditions, with incidents in snow rising from 78 to 101 and crashes on icy roads increasing from 40 to 53 year-over-year.

Weather

Clear1,372 (63.9%)
1.1%prior 1,357
Cloudy446 (20.8%)
1.6%prior 439
Rain170 (7.9%)
-13.7%prior 197
Snow101 (4.7%)
29.5%prior 78
Fog; Smog; Smoke17 (0.8%)
112.5%prior 8
Sleet; Hail13 (0.6%)
Other/Unknown10 (0.5%)
-23.1%prior 13
Freezing Rain or Freezing Drizzle8 (0.4%)
Blowing Sand; Soil; Dirt; Snow7 (0.3%)
Severe Crosswinds2 (0.1%)
-66.7%prior 6

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

Lighting

Daylight1,248 (58.2%)
0.7%prior 1,239
Dark - Roadway Not Lighted530 (24.7%)
4.3%prior 508
Dark - Lighted Roadway193 (9.0%)
-2.5%prior 198
Dawn/Dusk158 (7.4%)
12.9%prior 140
Other/Unknown13 (0.6%)
-13.3%prior 15
Dark - Unknown Roadway Lighting4 (0.2%)

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

Road Surface

Dry1,646 (76.7%)
0.9%prior 1,632
Wet356 (16.6%)
2.3%prior 348
Snow83 (3.9%)
22.1%prior 68
Ice53 (2.5%)
32.5%prior 40
Other/Unknown6 (0.3%)
-40.0%prior 10
Slush2 (0.1%)
-60.0%prior 5

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford (530), Chevrolet (492), and Honda (294) being the most common in the current period, mirroring the prior year's top makes. The age distribution of persons involved showed a slight shift, with an increase in the 16-20 age group (from 573 to 636) and the 55+ age groups. Conversely, there was a decrease in involvement for the 35-44 age group (from 583 to 532).

Top Vehicle Makes (3,370 vehicles)

1
FORD530 (15.7%)
2.7%prior 516
2
CHEVROLET492 (14.6%)
-5.0%prior 518
3
HONDA294 (8.7%)
6.1%prior 277
4
DODGE224 (6.6%)
13.7%prior 197
5
TOYOTA189 (5.6%)
-3.1%prior 195
6
KIA163 (4.8%)
1.2%prior 161
7
JEEP154 (4.6%)
19.4%prior 129
8
HYUNDAI123 (3.6%)
0.0%prior 123
9
GMC119 (3.5%)
-4.8%prior 125
10
NISSAN114 (3.4%)
-8.1%prior 124

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

193 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (4,211 persons with recorded sex)

Male2,350 (55.8%)
5.0%prior 2,239
Female1,861 (44.2%)
-1.5%prior 1,890

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 6, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 2,146
  • Total persons involved: 4,365
  • Total vehicles involved: 3,370

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 6, 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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Hancock County, OH Crash Report — 2022 | ThatCarHitMe.com