Yearly Traffic Safety Analysis

1,399 CRASHES IN
OHIO, OH
2022

All metrics benchmarked against2021

In Sandusky County, total vehicle crashes remained relatively stable, decreasing by 1.0% from 1,413 in 2021 to 1,399 in 2022. Despite this slight decrease in overall collisions, the severity of crashes worsened, with the number of fatalities increasing from 5 to 7 year-over-year. The most notable shift was this 40% increase in persons killed in traffic incidents.

1,399

-1.0%was 1,413

Total Crash Events

7

40.0%was 5

Persons Killed

517

14.9%was 450

Persons Injured

115

-6.5%was 123

Hit-and-Run Crashes

Note: "Persons Killed" (7) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) 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

Crash trends in Sandusky County showed a minor overall decrease in volume but an increase in severity between 2021 and 2022. Total crashes fell by 1.0% from 1,413 to 1,399. However, total injuries rose by 14.9% from 450 to 517, and fatalities increased by 40% from 5 to 7, indicating that collisions became more dangerous year-over-year.

115

Hit-and-Run Crashes — 2022

-6.5% vs prior (123)

Hit-and-run incidents saw a slight downward trend in Sandusky County. The total number of hit-and-run crashes decreased from 123 in 2021 to 115 in 2022. The hit-and-run rate, which measures these incidents as a percentage of all crashes, also declined from 8.7% in the prior year to 8.2% in the current year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1-100.0%

7

Motorists Killed

Prior: 475.0%

2

Pedestrians Injured

Prior: 8-75.0%

515

Motorists Injured

Prior: 44216.5%

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 timing of crashes showed minor shifts between the two periods. In 2022, the peak day for crashes was Thursday with 229 incidents, a change from Wednesday (239 crashes) in the prior year. The peak hour also shifted slightly earlier, moving from 4 p.m. (90 crashes) in 2021 to 3 p.m. (91 crashes) in 2022, though still within the afternoon travel period.

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

The severity of crashes increased from 2021 to 2022. The number of fatal crashes rose from 4 to 7, and the fatal crash rate nearly doubled from 0.28% to 0.50%. The proportion of crashes involving injuries also grew, with serious injury crashes increasing from 2.3% to 2.7% of all incidents and minor injury crashes rising from 13.2% to 14.4%. Consequently, the share of non-injury crashes decreased from 77.1% to 75.2%.

Outcome by Severity (Crash Events)

Fatal7fatal crashes0.5%
75.0%prior 4
Serious Injury38serious injury crashes2.7%
15.2%prior 33
Minor Injury202minor injury crashes14.4%
8.6%prior 186
Possible Injury100possible injury crashes7.1%
-1.0%prior 101
No Injury1,052no injury crashes75.2%
-3.4%prior 1,089

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 different environmental conditions remained largely consistent year-over-year. In both 2022 and 2021, the majority of crashes occurred in clear weather (61.8% and 63.4%, respectively) and on dry roads (78.2% and 77.5%, respectively). There were no significant shifts in the proportions of crashes happening during adverse weather, in darkness, or on wet or snowy road surfaces.

Weather

Clear865 (61.8%)
-3.5%prior 896
Cloudy313 (22.4%)
9.8%prior 285
Rain98 (7.0%)
-28.5%prior 137
Snow85 (6.1%)
18.1%prior 72
Fog; Smog; Smoke13 (0.9%)
116.7%prior 6
Sleet; Hail8 (0.6%)
Blowing Sand; Soil; Dirt; Snow7 (0.5%)
Severe Crosswinds6 (0.4%)
Other/Unknown3 (0.2%)
-62.5%prior 8
Freezing Rain or Freezing Drizzle1 (0.1%)

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

Lighting

Daylight785 (56.1%)
4.4%prior 752
Dark - Roadway Not Lighted422 (30.2%)
-7.0%prior 454
Dark - Lighted Roadway113 (8.1%)
-0.9%prior 114
Dawn/Dusk76 (5.4%)
-10.6%prior 85
Other/Unknown3 (0.2%)
-40.0%prior 5

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

Road Surface

Dry1,094 (78.2%)
-0.1%prior 1,095
Wet190 (13.6%)
-11.6%prior 215
Snow85 (6.1%)
16.4%prior 73
Ice22 (1.6%)
22.2%prior 18
Slush6 (0.4%)
20.0%prior 5
Other/Unknown2 (0.1%)
-66.7%prior 6

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 shifted slightly between periods. In 2022, Ford was the most common make with 403 vehicles, overtaking Chevrolet, which was number one in 2021 with 402 vehicles. The age demographics of people involved in crashes also saw minor changes, with a notable increase in the number of individuals in the 35-44 and 45-54 age groups, which grew from 384 to 406 and 326 to 394, respectively.

Top Vehicle Makes (2,190 vehicles)

1
FORD403 (18.4%)
3.9%prior 388
2
CHEVROLET362 (16.5%)
-10.0%prior 402
3
DODGE140 (6.4%)
-9.7%prior 155
4
HONDA110 (5%)
22.2%prior 90
5
FREIGHTLINER106 (4.8%)
8.2%prior 98
6
TOYOTA105 (4.8%)
22.1%prior 86
7
JEEP99 (4.5%)
16.5%prior 85
8
CHRYSLER85 (3.9%)
-7.6%prior 92
9
GMC71 (3.2%)
-2.7%prior 73
10
KIA66 (3%)
22.2%prior 54

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

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

Sex Distribution (2,805 persons with recorded sex)

Male1,627 (58.0%)
0.9%prior 1,613
Female1,178 (42.0%)
7.2%prior 1,099

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: 1,399
  • Total persons involved: 2,881
  • Total vehicles involved: 2,190

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

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