Yearly Traffic Safety Analysis

838 CRASHES IN
OHIO, OH
2025

All metrics benchmarked against2024

In 2025, Ottawa County recorded 838 total traffic crashes, a slight decrease from the 843 crashes documented in 2024. This represents a less than 1% change in overall crash volume. The most notable year-over-year shift was a significant reduction in traffic fatalities, which fell from four in the prior period to one in the current period.

838

-0.6%was 843

Total Crash Events

1

-75.0%was 4

Persons Killed

238

-13.8%was 276

Persons Injured

47

Hit-and-Run Crashes

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 crash volume for Ottawa County was stable, with total crashes decreasing by just five incidents (-0.6%) from 2024 to 2025. However, the severity of crashes lessened considerably. The number of people injured fell by 13.8%, from 276 to 238, and total fatalities decreased by 75%, from four to one.

47

Hit-and-Run Crashes — 2025

0.0% vs prior (47)

The frequency of hit-and-run crashes in Ottawa County was unchanged year-over-year. In both 2025 and 2024, official records show there were 47 hit-and-run incidents. This resulted in an identical hit-and-run rate of 5.6% of total crashes for both periods, indicating a stable trend.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 4-75.0%

238

Motorists Injured

Prior: 269-11.5%

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 showed some consistency and some shifts between the two periods. Friday remained the day with the highest number of crashes in both 2024 (147 crashes) and 2025 (135 crashes). However, the peak hour for collisions shifted from the 5 p.m. hour in the prior year to the 2 p.m. hour in the current year, which recorded 64 incidents.

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

Crash severity decreased notably from the prior year. The fatal crash rate dropped from 0.47% to 0.12%, corresponding to a drop from four fatal crashes to one. The proportion of crashes involving serious injuries also declined from 3.6% to 2.4%. Consequently, the share of crashes resulting in no injury increased from 79.2% in 2024 to 80.7% in 2025.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.1%
-75.0%prior 4
Serious Injury20serious injury crashes2.4%
-33.3%prior 30
Minor Injury93minor injury crashes11.1%
-4.1%prior 97
Possible Injury48possible injury crashes5.7%
9.1%prior 44
No Injury676no injury crashes80.7%
1.2%prior 668

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

While the proportion of crashes in daylight versus dark conditions remained similar year-over-year, there was a noticeable shift in weather and road surface conditions. The share of crashes occurring in clear weather on dry roads decreased compared to the prior year. Conversely, incidents during adverse conditions increased, with crashes in snow rising from 21 to 61 and collisions on snowy road surfaces increasing from 19 to 57.

Weather

Clear557 (66.5%)
-9.6%prior 616
Cloudy140 (16.7%)
16.7%prior 120
Snow61 (7.3%)
190.5%prior 21
Rain54 (6.4%)
-14.3%prior 63
Freezing Rain or Freezing Drizzle7 (0.8%)
Other/Unknown7 (0.8%)
-12.5%prior 8
Fog; Smog; Smoke6 (0.7%)
-45.5%prior 11
Blowing Sand; Soil; Dirt; Snow3 (0.4%)
Severe Crosswinds3 (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

Daylight474 (56.6%)
1.9%prior 465
Dark - Roadway Not Lighted223 (26.6%)
-2.2%prior 228
Dawn/Dusk72 (8.6%)
7.5%prior 67
Dark - Lighted Roadway56 (6.7%)
-16.4%prior 67
Dark - Unknown Roadway Lighting11 (1.3%)
10.0%prior 10
Other/Unknown2 (0.2%)
-66.7%prior 6

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

Road Surface

Dry644 (76.8%)
-7.9%prior 699
Wet109 (13.0%)
-0.9%prior 110
Snow57 (6.8%)
200.0%prior 19
Ice23 (2.7%)
228.6%prior 7
Other/Unknown2 (0.2%)
-66.7%prior 6
Sand; Mud; Dirt; Oil; Gravel1 (0.1%)
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

The primary vehicle makes involved in crashes, led by Ford and Chevrolet, remained consistent between both periods. Similarly, the distribution of involved vehicle types, such as Passenger Cars and Sport Utility Vehicles, saw minimal change. An analysis of persons involved in crashes revealed a demographic shift, with the proportion of individuals aged 16-20 increasing from 11.6% to 13.7% and the share of those aged 65 and older decreasing from 18.7% to 15.8%.

Top Vehicle Makes (1,220 vehicles)

1
FORD250 (20.5%)
-6.4%prior 267
2
CHEVROLET222 (18.2%)
6.2%prior 209
3
TOYOTA73 (6%)
2.8%prior 71
4
DODGE63 (5.2%)
12.5%prior 56
5
JEEP59 (4.8%)
-20.3%prior 74
6
HONDA59 (4.8%)
-33.0%prior 88
7
GMC56 (4.6%)
36.6%prior 41
8
KIA48 (3.9%)
37.1%prior 35
9
CHRYSLER35 (2.9%)
16.7%prior 30
10
HYUNDAI34 (2.8%)
112.5%prior 16

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

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

Sex Distribution (1,576 persons with recorded sex)

Male962 (61.0%)
1.1%prior 952
Female614 (39.0%)
-8.4%prior 670

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 6, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 838
  • Total persons involved: 1,611
  • Total vehicles involved: 1,220

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 6, 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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Ottawa County, OH Crash Report — 2025 | ThatCarHitMe.com