Monthly Traffic Safety Analysis

18,533 CRASHES IN
OHIO, OH
MARCH 2025

All metrics benchmarked againstMarch 2024

In March 2025, Ohio recorded 18,533 total traffic crashes, a slight increase of 0.9% from the 18,366 crashes documented in March 2024. While the overall crash volume remained relatively stable, the most notable year-over-year shift was a significant 18.3% decrease in total fatalities, which fell from 93 to 76. Total injuries saw a marginal increase of 0.8%, from 6,407 to 6,458.

18,533

0.9%was 18,366

Total Crash Events

76

-18.3%was 93

Persons Killed

6,458

0.8%was 6,407

Persons Injured

3,410

1.3%was 3,366

Hit-and-Run Crashes

Note: "Persons Killed" (76) counts individual fatalities across all crash events. "Fatal" in the severity table below (69) 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 · 2025-03-01 to 2025-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Year-over-year data indicates a stable trend in overall crash volume, with a minor increase of 167 incidents, or 0.9%, from March 2024 to March 2025. Despite this stability, outcomes improved, as total fatalities decreased by 18.3% (from 93 to 76). Conversely, the number of people injured saw a slight uptick of 0.8% (from 6,407 to 6,458).

3,410

Hit-and-Run Crashes — March 2025

1.3% vs prior (3,366)

Hit-and-run incidents showed a slight upward trend compared to the previous year. The total number of hit-and-run crashes increased from 3,366 in March 2024 to 3,410 in March 2025. The hit-and-run rate, which measures the percentage of all crashes that are hit-and-runs, also edged up slightly from 18.3% to 18.4%.

Vulnerable Road User Casualties

8

Pedestrians Killed

Prior: 13-38.5%

68

Motorists Killed

Prior: 80-15.0%

168

Pedestrians Injured

Prior: 169-0.6%

6,290

Motorists Injured

Prior: 6,2380.8%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-03-01 to 2025-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed a distinct shift in the peak day of the week. In March 2025, Monday was the busiest day with 2,971 crashes, whereas in the prior year, Friday had the highest volume at 3,492 crashes. The peak hour for collisions, however, remained consistent at 4 p.m. in both periods, with a slight increase in incidents during that hour from 1,464 to 1,521.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-03-01 to 2025-03-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-03-01 to 2025-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shifted slightly year-over-year. The proportion of fatal crashes decreased, accounting for 0.4% of all crashes (69 incidents) in March 2025 compared to 0.5% (88 incidents) in March 2024. Conversely, crashes resulting in serious injuries saw a small proportional increase, rising from 2.1% (379 crashes) to 2.2% (411 crashes) of the total. Crashes with no injuries also made up a slightly larger share of the total, increasing from 74.7% to 75.4%.

Severity is per crash event (most severe injury). 69 fatal crash events resulted in 76 persons killed.

Outcome by Severity (Crash Events)

Fatal69fatal crashes0.4%
-21.6%prior 88
Serious Injury411serious injury crashes2.2%
8.4%prior 379
Minor Injury2,319minor injury crashes12.5%
0.0%prior 2,318
Possible Injury1,765possible injury crashes9.5%
-5.3%prior 1,864
No Injury13,969no injury crashes75.4%
1.8%prior 13,717

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-03-01 to 2025-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-03-01 to 2025-03-31 · Most severe injury per crash record

Road & Environmental Conditions

Comparing crash conditions year-over-year reveals a shift towards incidents occurring in clearer conditions. In March 2025, 65.3% of crashes occurred in clear weather and 81.8% on dry roads, up from 58.3% and 75.4% respectively in March 2024. Correspondingly, the proportion of crashes in the rain decreased from 12.8% to 9.2%, and crashes on wet roads fell from 21.5% to 15.7%. The percentage of crashes occurring in daylight remained stable at approximately 65%.

Weather

Clear12,099 (65.3%)
13.0%prior 10,703
Cloudy4,077 (22.0%)
-8.6%prior 4,463
Rain1,707 (9.2%)
-27.4%prior 2,350
Snow366 (2.0%)
-26.2%prior 496
Other/Unknown195 (1.1%)
2.1%prior 191
Sleet; Hail25 (0.1%)
-10.7%prior 28
Fog; Smog; Smoke24 (0.1%)
-78.0%prior 109
Freezing Rain or Freezing Drizzle21 (0.1%)
31.3%prior 16
Severe Crosswinds17 (0.1%)
88.9%prior 9
Blowing Sand; Soil; Dirt; Snow2 (0.0%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-03-01 to 2025-03-31 · Weather condition at time of crash

Lighting

Daylight12,105 (65.3%)
1.9%prior 11,883
Dark - Lighted Roadway2,892 (15.6%)
-5.1%prior 3,046
Dark - Roadway Not Lighted2,208 (11.9%)
2.3%prior 2,159
Dawn/Dusk1,064 (5.7%)
4.5%prior 1,018
Other/Unknown157 (0.8%)
-2.5%prior 161
Dark - Unknown Roadway Lighting107 (0.6%)
8.1%prior 99

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-03-01 to 2025-03-31 · Lighting condition field

Road Surface

Dry15,154 (81.8%)
9.4%prior 13,846
Wet2,906 (15.7%)
-26.5%prior 3,952
Ice156 (0.8%)
5.4%prior 148
Other/Unknown151 (0.8%)
-2.6%prior 155
Snow138 (0.7%)
-39.5%prior 228
Sand; Mud; Dirt; Oil; Gravel13 (0.1%)
8.3%prior 12
Slush9 (0.0%)
-43.8%prior 16
Water (Standing; Moving)6 (0.0%)
-33.3%prior 9

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-03-01 to 2025-03-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes—Chevrolet, Ford, and Honda—remained the same in both March 2025 and March 2024, with each showing a slight decrease in total involvement. While Passenger Cars remained the most common vehicle type, their numbers fell from 16,153 to 15,596, while Sport Utility Vehicles saw an increase from 8,427 to 9,090. The 26-34 age group continued to be the most represented among persons involved in crashes, though the 65+ age group saw its count increase from 4,484 to 4,737.

Top Vehicle Makes (33,205 vehicles)

1
CHEVROLET4,642 (14%)
-2.6%prior 4,765
2
FORD4,411 (13.3%)
-2.2%prior 4,510
3
HONDA3,039 (9.2%)
-3.5%prior 3,149
4
TOYOTA2,664 (8%)
5.0%prior 2,536
5
NISSAN1,564 (4.7%)
-2.0%prior 1,596
6
JEEP1,437 (4.3%)
4.8%prior 1,371
7
KIA1,430 (4.3%)
8.7%prior 1,316
8
DODGE1,425 (4.3%)
-5.8%prior 1,513
9
HYUNDAI1,373 (4.1%)
7.9%prior 1,273
10
GMC999 (3%)
14.3%prior 874

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-03-01 to 2025-03-31 · Vehicle unit records

2,911 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (39,037 persons with recorded sex)

Male21,687 (55.6%)
1.4%prior 21,380
Female17,350 (44.4%)
-0.6%prior 17,447

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-03-01 to 2025-03-31 · 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: 2025-03-01 through 2025-03-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2025-03-01 through 2025-03-31 (31 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 18,533
  • Total persons involved: 41,429
  • Total vehicles involved: 33,205

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: March 2025." Published July 5, 2026. Reporting period: 2025-03-01 to 2025-03-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/march-2025-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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Ohio (Statewide) Crash Report — March 2025 | ThatCarHitMe.com