Yearly Traffic Safety Analysis

753 CRASHES IN
OHIO, OH
2025

All metrics benchmarked against2024

In Wyandot County, total traffic crashes increased by 16.6% from 646 in 2024 to 753 in 2025. This rise was accompanied by a 24.4% increase in total injuries, which grew from 156 to 194. The most notable shift was this significant increase in both overall crash volume and the number of people injured, while fatalities remained unchanged at two for both periods.

753

16.6%was 646

Total Crash Events

2

Persons Killed

194

24.4%was 156

Persons Injured

36

-5.3%was 38

Hit-and-Run Crashes

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

The overall trend in Wyandot County shows a worsening of traffic safety year-over-year. Total crashes rose by 107 incidents, representing a 16.6% increase from the previous year. While fatalities held steady at two, the number of persons injured increased by 24.4%, climbing from 156 to 194.

36

Hit-and-Run Crashes — 2025

-5.3% vs prior (38)

Hit-and-run incidents showed a downward trend in both count and rate. The absolute number of hit-and-run crashes decreased from 38 in 2024 to 36 in 2025. As a percentage of all crashes, the hit-and-run rate declined from 5.9% to 4.8% year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

2

Motorists Killed

Prior: 20.0%

2

Pedestrians Injured

Prior: 20.0%

192

Motorists Injured

Prior: 15424.7%

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

The temporal patterns of crashes shifted between the two periods. The peak day for crashes moved from Friday (129 crashes) in 2024 to Wednesday (129 crashes) in 2025. The peak hour for collisions also shifted later into the evening, from 6 p.m. in the prior year (50 crashes) to 8 p.m. in the current year (59 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 number of fatal crashes remained constant at two for both years, the overall severity profile changed. The fatal crash rate saw a slight decrease from 0.31% to 0.27% due to the higher total number of crashes. The number of serious injury crashes declined from 24 to 19, but minor injury crashes increased substantially from 42 to 69, contributing to a 24.4% overall rise in injuries.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.3%
0.0%prior 2
Serious Injury19serious injury crashes2.5%
-20.8%prior 24
Minor Injury69minor injury crashes9.2%
64.3%prior 42
Possible Injury49possible injury crashes6.5%
25.6%prior 39
No Injury614no injury crashes81.5%
13.9%prior 539

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 year-over-year increase in crashes occurred predominantly in favorable conditions. Crashes on dry roads rose from 486 to 581, and those in clear weather increased from 386 to 503. Consequently, the proportion of crashes on dry roads grew from 75.2% to 77.2% of all incidents. The distribution of crashes by lighting conditions remained relatively stable, with daylight crashes accounting for 46.9% of the total in 2025 compared to 44.4% in 2024.

Weather

Clear503 (66.8%)
30.3%prior 386
Cloudy142 (18.9%)
1.4%prior 140
Rain45 (6.0%)
-29.7%prior 64
Snow41 (5.4%)
10.8%prior 37
Fog; Smog; Smoke15 (2.0%)
50.0%prior 10
Other/Unknown4 (0.5%)
Sleet; Hail2 (0.3%)
Freezing Rain or Freezing Drizzle1 (0.1%)

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

Lighting

Daylight353 (46.9%)
23.0%prior 287
Dark - Roadway Not Lighted294 (39.0%)
11.8%prior 263
Dark - Lighted Roadway51 (6.8%)
4.1%prior 49
Dawn/Dusk49 (6.5%)
14.0%prior 43
Dark - Unknown Roadway Lighting3 (0.4%)
Other/Unknown3 (0.4%)

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

Road Surface

Dry581 (77.2%)
19.5%prior 486
Wet92 (12.2%)
-13.2%prior 106
Snow56 (7.4%)
100.0%prior 28
Ice19 (2.5%)
0.0%prior 19
Other/Unknown3 (0.4%)
Slush1 (0.1%)
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

Vehicle make involvement shifted, with Chevrolet (167 vehicles) surpassing Ford (151 vehicles) as the most common make in crashes, a reversal from the previous year where Ford led. An analysis of persons involved shows the largest increase in the 26-34 age group, which grew from 158 to 206 individuals. The 65+ age group also saw a notable increase in involvement, from 146 to 173 persons.

Top Vehicle Makes (1,015 vehicles)

1
CHEVROLET167 (16.5%)
26.5%prior 132
2
FORD151 (14.9%)
-1.9%prior 154
3
DODGE87 (8.6%)
38.1%prior 63
4
HONDA83 (8.2%)
7.8%prior 77
5
TOYOTA57 (5.6%)
21.3%prior 47
6
KIA45 (4.4%)
32.4%prior 34
7
JEEP41 (4%)
5.1%prior 39
8
GMC38 (3.7%)
0.0%prior 38
9
FREIGHTLINER36 (3.5%)
28.6%prior 28
10
HYUNDAI32 (3.2%)
100.0%prior 16

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

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

Sex Distribution (1,330 persons with recorded sex)

Male767 (57.7%)
14.0%prior 673
Female563 (42.3%)
30.0%prior 433

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: 753
  • Total persons involved: 1,358
  • Total vehicles involved: 1,015

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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Wyandot County, OH Crash Report — 2025 | ThatCarHitMe.com