Yearly Traffic Safety Analysis

1,691 CRASHES IN
OHIO, OH
2025

All metrics benchmarked against2024

In Sandusky County, total vehicle crashes increased from 1,240 in 2024 to 1,691 in 2025, a rise of 36.4%. This surge in collisions was accompanied by an increase in both injuries, from 412 to 569, and fatalities, which rose from 9 to 11. The most significant year-over-year shift was the substantial growth in the overall number of crashes, which occurred across most categories.

1,691

36.4%was 1,240

Total Crash Events

11

22.2%was 9

Persons Killed

569

38.1%was 412

Persons Injured

117

18.2%was 99

Hit-and-Run Crashes

Note: "Persons Killed" (11) counts individual fatalities across all crash events. "Fatal" in the severity table below (8) 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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Sandusky County showed a significant upward trend year-over-year. The total number of crashes rose by 36.4%, from 1,240 to 1,691. This increase was mirrored by a 38.1% rise in total injuries and a 22.2% increase in fatalities.

117

Hit-and-Run Crashes — 2025

18.2% vs prior (99)

The absolute number of hit-and-run crashes increased from 99 in 2024 to 117 in 2025. However, due to the larger increase in total crashes, the hit-and-run rate as a percentage of all incidents trended downward. The rate decreased from 8.0% in the prior period to 6.9% in the current period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 2-100.0%

11

Motorists Killed

Prior: 757.1%

8

Pedestrians Injured

Prior: 560.0%

561

Motorists Injured

Prior: 40737.8%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns remained broadly consistent, with Friday being the most frequent day for crashes in both 2024 (220 crashes) and 2025 (280 crashes). The afternoon commute continued to be the peak time for incidents, though the specific peak hour shifted slightly earlier from 3 PM in the prior year (95 crashes) to 2 PM in the current year (124 crashes).

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of people killed in crashes increased from 9 to 11 year-over-year, the number of fatal crash events remained stable at 8 for both periods. Consequently, the fatal crash rate per 100 crashes decreased from 0.65 to 0.47. The proportion of crashes involving any level of injury also saw a slight decrease, as property-damage-only crashes grew from 76.0% to 77.4% of all incidents.

Severity is per crash event (most severe injury). 8 fatal crash events resulted in 11 persons killed.

Outcome by Severity (Crash Events)

Fatal8fatal crashes0.5%
0.0%prior 8
Serious Injury59serious injury crashes3.5%
22.9%prior 48
Minor Injury194minor injury crashes11.5%
28.5%prior 151
Possible Injury121possible injury crashes7.2%
33.0%prior 91
No Injury1,309no injury crashes77.4%
39.0%prior 942

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The conditions under which crashes occurred remained largely unchanged between the two periods. In both 2025 and 2024, the majority of collisions happened in daylight (55.1% and 56.9% respectively) and on dry road surfaces (75.0% and 79.4% respectively). There was no significant shift indicating that the overall increase in crashes was driven by a rise in adverse weather or lighting conditions.

Weather

Clear1,086 (64.2%)
34.4%prior 808
Cloudy269 (15.9%)
8.9%prior 247
Rain142 (8.4%)
25.7%prior 113
Snow115 (6.8%)
130.0%prior 50
Freezing Rain or Freezing Drizzle24 (1.4%)
Fog; Smog; Smoke15 (0.9%)
0.0%prior 15
Other/Unknown13 (0.8%)
Blowing Sand; Soil; Dirt; Snow13 (0.8%)
Severe Crosswinds8 (0.5%)
Sleet; Hail6 (0.4%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight931 (55.1%)
32.1%prior 705
Dark - Roadway Not Lighted485 (28.7%)
41.4%prior 343
Dark - Lighted Roadway135 (8.0%)
35.0%prior 100
Dawn/Dusk115 (6.8%)
36.9%prior 84
Dark - Unknown Roadway Lighting18 (1.1%)
200.0%prior 6
Other/Unknown7 (0.4%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry1,269 (75.0%)
29.0%prior 984
Wet233 (13.8%)
27.3%prior 183
Snow125 (7.4%)
212.5%prior 40
Ice49 (2.9%)
96.0%prior 25
Slush9 (0.5%)
28.6%prior 7
Other/Unknown5 (0.3%)
Water (Standing; Moving)1 (0.1%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The top five vehicle makes involved in crashes remained the same, with Chevrolet (469 vehicles) overtaking Ford (445 vehicles) for the most-involved make in 2025. The age distribution of persons involved in crashes also stayed consistent, with no single age group experiencing a disproportionate increase relative to the overall growth in crash-involved individuals. For example, the 65+ age group represented 13.5% of persons in the current period, nearly identical to the 13.6% in the prior period.

Top Vehicle Makes (2,620 vehicles)

1
CHEVROLET469 (17.9%)
46.1%prior 321
2
FORD445 (17%)
31.3%prior 339
3
DODGE152 (5.8%)
31.0%prior 116
4
JEEP143 (5.5%)
45.9%prior 98
5
HONDA134 (5.1%)
21.8%prior 110
6
TOYOTA125 (4.8%)
54.3%prior 81
7
FREIGHTLINER113 (4.3%)
27.0%prior 89
8
KIA106 (4%)
89.3%prior 56
9
GMC87 (3.3%)
24.3%prior 70
10
CHRYSLER82 (3.1%)
22.4%prior 67

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (3,451 persons with recorded sex)

Male1,984 (57.5%)
30.6%prior 1,519
Female1,467 (42.5%)
44.4%prior 1,016

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 1,691
  • Total persons involved: 3,534
  • Total vehicles involved: 2,620

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: 2025." Published July 5, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/2025-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 — 2025 | ThatCarHitMe.com