Yearly Traffic Safety Analysis

3,203 CRASHES IN
GREEN, OH
2025

All metrics benchmarked against2024

In 2025, there were 3,203 total crashes, a slight increase of 0.82% compared to 3,177 crashes in 2024. Total fatalities saw a significant rise, with 17 fatalities in 2025, marking a 70% increase from 10 fatalities in 2024. Injuries remained relatively stable, with 969 in 2025 compared to 967 in the prior year, a 0.21% increase.

3,203

0.8%was 3,177

Total Crash Events

17

70.0%was 10

Persons Killed

969

0.2%was 967

Persons Injured

396

5.6%was 375

Hit-and-Run Crashes

Note: "Persons Killed" (17) counts individual fatalities across all crash events. "Fatal" in the severity table below (16) 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

Overall, crash trends show a slight increase in total incidents and a notable rise in fatalities year-over-year. Total crashes increased by 0.82% from 3,177 to 3,203, while total fatalities surged by 70% from 10 to 17. Total injuries experienced a marginal increase of 0.21%, rising from 967 to 969.

396

Hit-and-Run Crashes — 2025

5.6% vs prior (375)

Hit-and-run crashes increased by 5.6% year-over-year, rising from 375 incidents in 2024 to 396 in 2025. The hit-and-run rate also saw an upward trend, increasing by 0.6 percentage points from 11.8% to 12.4% of all crashes.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 1100.0%

15

Motorists Killed

Prior: 966.7%

13

Pedestrians Injured

Prior: 17-23.5%

956

Motorists Injured

Prior: 9500.6%

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 peak day for crashes remained Friday in both periods, though the number of crashes on Fridays decreased by 4.86% from 556 in 2024 to 529 in 2025. The peak hour for crashes also remained 4 PM, with a slight increase in incidents from 264 in 2024 to 273 in 2025. Monthly patterns showed fluctuations, with some months experiencing increased crashes and others decreased, such as a 19.8% increase in crashes on Mondays.

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

The fatal crash rate increased from 0.3% in 2024 to 0.5% in 2025, reflecting a 60% increase in fatal crashes from 10 to 16. Serious injury crashes (severity A) decreased by 10.77% from 65 to 58, while minor injury crashes (severity B) saw a 2.92% decrease from 343 to 333. Conversely, possible injury crashes (severity C) increased by 12.64% from 269 to 303.

Severity is per crash event (most severe injury). 16 fatal crash events resulted in 17 persons killed.

Outcome by Severity (Crash Events)

Fatal16fatal crashes0.5%
60.0%prior 10
Serious Injury58serious injury crashes1.8%
-10.8%prior 65
Minor Injury333minor injury crashes10.4%
-2.9%prior 343
Possible Injury303possible injury crashes9.5%
12.6%prior 269
No Injury2,493no injury crashes77.8%
0.1%prior 2,490

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

Crashes occurring in snowy weather conditions increased significantly by 97.1%, from 105 in 2024 to 207 in 2025. Correspondingly, crashes on snowy road surfaces also rose substantially by 148%, from 77 to 191, and crashes on icy surfaces more than doubled, increasing by 116.67% from 24 to 52. Crashes in rainy weather decreased by 24.6%, from 399 to 301.

Weather

Clear1,984 (61.9%)
1.9%prior 1,947
Cloudy661 (20.6%)
-4.8%prior 694
Rain301 (9.4%)
-24.6%prior 399
Snow207 (6.5%)
97.1%prior 105
Other/Unknown17 (0.5%)
30.8%prior 13
Fog; Smog; Smoke15 (0.5%)
36.4%prior 11
Freezing Rain or Freezing Drizzle8 (0.2%)
Sleet; Hail6 (0.2%)
Blowing Sand; Soil; Dirt; Snow2 (0.1%)
Severe Crosswinds2 (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

Daylight2,189 (68.3%)
1.7%prior 2,152
Dark - Roadway Not Lighted448 (14.0%)
-3.9%prior 466
Dark - Lighted Roadway322 (10.1%)
-7.2%prior 347
Dawn/Dusk209 (6.5%)
11.2%prior 188
Dark - Unknown Roadway Lighting18 (0.6%)
0.0%prior 18
Other/Unknown17 (0.5%)
183.3%prior 6

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

Road Surface

Dry2,348 (73.3%)
-2.5%prior 2,407
Wet601 (18.8%)
-8.8%prior 659
Snow191 (6.0%)
148.1%prior 77
Ice52 (1.6%)
116.7%prior 24
Other/Unknown5 (0.2%)
Slush4 (0.1%)
-20.0%prior 5
Sand; Mud; Dirt; Oil; Gravel1 (0.0%)
Water (Standing; Moving)1 (0.0%)

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

Vehicles & Demographics

The total number of vehicles involved in crashes saw a minor increase of 0.23%, from 5,595 in 2024 to 5,608 in 2025. Passenger car involvement decreased by 5.7%, while Sport Utility Vehicle involvement increased by 8.43%. In terms of age distribution, there was a notable decrease of 8.84% in persons aged 0-15 involved in crashes, and a 6.09% decrease for those aged 16-20, while the 65+ age group saw a 4.19% increase.

Top Vehicle Makes (5,608 vehicles)

1
CHEVROLET802 (14.3%)
5.2%prior 762
2
FORD768 (13.7%)
-4.5%prior 804
3
HONDA632 (11.3%)
3.3%prior 612
4
TOYOTA567 (10.1%)
-3.6%prior 588
5
NISSAN286 (5.1%)
4.8%prior 273
6
KIA283 (5%)
6.4%prior 266
7
JEEP230 (4.1%)
2.7%prior 224
8
HYUNDAI221 (3.9%)
-3.1%prior 228
9
DODGE176 (3.1%)
-31.5%prior 257
10
GMC172 (3.1%)
47.0%prior 117

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

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

Sex Distribution (6,936 persons with recorded sex)

Male3,747 (54.0%)
0.9%prior 3,712
Female3,189 (46.0%)
-0.1%prior 3,192

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: Green, OH
  • Total crash records analyzed: 3,203
  • Total persons involved: 7,193
  • Total vehicles involved: 5,608

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). "Green, 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/green/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

ThatCarHitMe.com · An Injuria.ai Company

Green, OH Crash Report — 2025 | ThatCarHitMe.com