Monthly Traffic Safety Analysis

19,728 CRASHES IN
OHIO, OH
APRIL 2025

All metrics benchmarked againstApril 2024

In April 2025, Ohio recorded 19,728 total traffic crashes, a figure nearly identical to the 19,735 crashes reported in April 2024, representing a decrease of less than 0.1%. While the overall crash volume remained stable, the most notable year-over-year change was a significant 24.2% decrease in fatalities, which fell from 99 to 75. Total injuries also saw a decline, dropping 3.5% from 7,298 to 7,042.

19,728

-0.0%was 19,735

Total Crash Events

75

-24.2%was 99

Persons Killed

7,042

-3.5%was 7,298

Persons Injured

3,437

-0.3%was 3,446

Hit-and-Run Crashes

Note: "Persons Killed" (75) counts individual fatalities across all crash events. "Fatal" in the severity table below (70) 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 · 2025-04-01 to 2025-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Year-over-year data for April indicates a stable trend in the total number of crashes, with a marginal decrease of just 7 incidents from 19,735 in 2024 to 19,728 in 2025. However, the severity of these incidents lessened, as demonstrated by a 24.2% decrease in fatalities and a 3.5% decrease in total injuries compared to the same month in the prior year.

3,437

Hit-and-Run Crashes — April 2025

-0.3% vs prior (3,446)

The frequency of hit-and-run incidents remained remarkably stable year-over-year. In April 2025, there were 3,437 hit-and-run crashes, accounting for 17.4% of all crashes. This is almost identical to April 2024, which recorded 3,446 hit-and-run crashes at a rate of 17.5%, indicating no significant trend change in this metric.

Vulnerable Road User Casualties

18

Pedestrians Killed

Prior: 1612.5%

57

Motorists Killed

Prior: 83-31.3%

186

Pedestrians Injured

Prior: 192-3.1%

6,856

Motorists Injured

Prior: 7,106-3.5%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-04-01 to 2025-04-30 · 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 some shifts between the two periods. The peak day for crashes moved from Tuesday (3,579 crashes) in April 2024 to Wednesday (3,529 crashes) in April 2025. The peak hour for collisions also shifted slightly, from the 3 p.m. hour (1,709 crashes) in the prior year to the 4 p.m. hour (1,703 crashes) in the current period, keeping the afternoon commute as the most frequent time for incidents.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-04-01 to 2025-04-30 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-04-01 to 2025-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes decreased in April 2025 compared to the previous year. The number of fatal crashes fell from 90 to 70, and their share of all crashes dropped from 0.5% to 0.4%. While serious injury crashes saw a slight increase from 504 to 524, crashes resulting in minor or possible injuries both decreased. Consequently, the proportion of crashes with no reported injuries increased from 73.8% to 74.6% of the total.

Severity is per crash event (most severe injury). 70 fatal crash events resulted in 75 persons killed.

Outcome by Severity (Crash Events)

Fatal70fatal crashes0.4%
-22.2%prior 90
Serious Injury524serious injury crashes2.7%
4.0%prior 504
Minor Injury2,552minor injury crashes12.9%
-1.3%prior 2,586
Possible Injury1,868possible injury crashes9.5%
-5.9%prior 1,986
No Injury14,714no injury crashes74.6%
1.0%prior 14,569

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-04-01 to 2025-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-04-01 to 2025-04-30 · Most severe injury per crash record

Road & Environmental Conditions

Crash conditions varied slightly year-over-year, largely reflecting different weather patterns. In April 2025, there were fewer crashes in the rain (2,747) and on wet roads (3,998) compared to April 2024, which saw 3,310 rain-related crashes and 4,910 on wet surfaces. Conversely, crashes on dry roads increased from 14,644 to 15,315. Lighting conditions remained broadly similar, though crashes on unlit dark roadways increased from 1,799 to 1,946.

Weather

Clear11,770 (59.7%)
1.7%prior 11,569
Cloudy4,853 (24.6%)
5.4%prior 4,606
Rain2,747 (13.9%)
-17.0%prior 3,310
Other/Unknown182 (0.9%)
16.7%prior 156
Snow53 (0.3%)
89.3%prior 28
Fog; Smog; Smoke53 (0.3%)
253.3%prior 15
Freezing Rain or Freezing Drizzle32 (0.2%)
540.0%prior 5
Severe Crosswinds24 (0.1%)
200.0%prior 8
Sleet; Hail14 (0.1%)
-63.2%prior 38

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-04-01 to 2025-04-30 · Weather condition at time of crash

Lighting

Daylight14,113 (71.5%)
-1.3%prior 14,299
Dark - Lighted Roadway2,379 (12.1%)
-3.1%prior 2,455
Dark - Roadway Not Lighted1,946 (9.9%)
8.2%prior 1,799
Dawn/Dusk1,056 (5.4%)
8.2%prior 976
Other/Unknown148 (0.8%)
25.4%prior 118
Dark - Unknown Roadway Lighting86 (0.4%)
-2.3%prior 88

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-04-01 to 2025-04-30 · Lighting condition field

Road Surface

Dry15,315 (77.6%)
4.6%prior 14,644
Wet3,998 (20.3%)
-18.6%prior 4,910
Ice230 (1.2%)
2775.0%prior 8
Other/Unknown121 (0.6%)
3.4%prior 117
Water (Standing; Moving)33 (0.2%)
-8.3%prior 36
Snow20 (0.1%)
122.2%prior 9
Sand; Mud; Dirt; Oil; Gravel10 (0.1%)
11.1%prior 9
Slush1 (0.0%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-04-01 to 2025-04-30 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes were consistent between the two periods, with Chevrolet, Ford, and Honda remaining the top three. Chevrolet (4,893) and Ford (4,782) involvements saw minor decreases from the prior year's 5,037 and 4,821, respectively. The age distribution of all persons involved in crashes also remained stable, with no significant proportional shifts observed among age groups like '16-20' (5,209 current vs 5,270 prior) or '26-34' (6,966 current vs 7,074 prior).

Top Vehicle Makes (35,684 vehicles)

1
CHEVROLET4,893 (13.7%)
-2.9%prior 5,037
2
FORD4,782 (13.4%)
-0.8%prior 4,821
3
HONDA3,357 (9.4%)
2.9%prior 3,263
4
TOYOTA2,870 (8%)
0.1%prior 2,867
5
NISSAN1,684 (4.7%)
-0.7%prior 1,696
6
KIA1,557 (4.4%)
9.3%prior 1,425
7
JEEP1,557 (4.4%)
4.4%prior 1,492
8
DODGE1,434 (4%)
-10.9%prior 1,609
9
HYUNDAI1,338 (3.7%)
-3.3%prior 1,384
10
OTHER/UNKNOWN1,032 (2.9%)
-3.2%prior 1,066

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-04-01 to 2025-04-30 · Vehicle unit records

3,011 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (42,268 persons with recorded sex)

Male23,500 (55.6%)
-0.6%prior 23,644
Female18,768 (44.4%)
-2.6%prior 19,270

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-04-01 to 2025-04-30 · 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: 2025-04-01 through 2025-04-30
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2025-04-01 through 2025-04-30 (30 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 19,728
  • Total persons involved: 44,726
  • Total vehicles involved: 35,684

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: April 2025." Published July 5, 2026. Reporting period: 2025-04-01 to 2025-04-30. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/april-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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