Yearly Traffic Safety Analysis

1,273 CRASHES IN
OHIO, OH
2024

All metrics benchmarked against2023

In 2024, Belmont County recorded 1,273 traffic crashes, a 6.8% decrease from the 1,366 crashes reported in 2023. While overall collisions and total injuries declined, the number of fatalities increased from 3 to 4 year-over-year. Total injuries saw a slight reduction from 444 in 2023 to 429 in 2024.

1,273

▼ -6.8%was 1,366

Total Crash Events

4

▲ 33.3%was 3

Persons Killed

429

▼ -3.4%was 444

Persons Injured

108

▼ -15.0%was 127

Hit-and-Run Crashes

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in traffic crashes in Belmont County is downward year-over-year. Total crashes decreased by 6.8%, from 1,366 in 2023 to 1,273 in 2024. Similarly, total injuries declined by 3.4% from 444 to 429, although the number of individuals killed in crashes increased from 3 to 4.

108

Hit-and-Run Crashes — 2024

▼ -15.0% vs prior (127)

Hit-and-run incidents decreased in both absolute numbers and as a percentage of total crashes from 2023 to 2024. There were 108 hit-and-run crashes in 2024, down from 127 in the prior year. This represents a downward trend in the hit-and-run rate, which fell from 9.3% of all crashes in 2023 to 8.5% in 2024.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

3

Motorists Killed

Prior: 3→0.0%

3

Pedestrians Injured

Prior: 5↓-40.0%

426

Motorists Injured

Prior: 439↓-3.0%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some shifts between 2023 and 2024. Friday remained the peak day for crashes in both periods, with 238 incidents in 2024 compared to 230 in 2023. The peak hour for crashes shifted slightly later, from the 4 p.m. hour in 2023 (108 crashes) to the 5 p.m. hour in 2024 (98 crashes).

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shows a mixed picture year-over-year. The fatal crash rate increased from 0.22% in 2023 to 0.31% in 2024, with fatal crashes rising from 3 to 4. The proportion of serious injury crashes remained stable at 2.4% in 2024 compared to 2.3% in 2023. Crashes resulting in no injury decreased as a proportion of all incidents, from 75.7% in 2023 to 74.6% in 2024.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.3%
↑33.3%prior 3
Serious Injury30serious injury crashes2.4%
↓-3.2%prior 31
Minor Injury178minor injury crashes14%
↓-7.8%prior 193
Possible Injury111possible injury crashes8.7%
↑5.7%prior 105
No Injury950no injury crashes74.6%
↓-8.1%prior 1,034

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather on dry roads during daylight. However, there was a notable shift in crashes on wintry road surfaces; the proportion of crashes on snow or ice increased from 2.7% in 2023 (37 crashes) to 5.3% in 2024 (68 crashes). Correspondingly, crashes occurring during snowy weather conditions rose from 3.4% to 4.6% of the total. The distribution of crashes by lighting conditions remained largely consistent, with about 64% of incidents happening in daylight in both periods.

Weather

Clear709 (55.7%)
↓-13.2%prior 817
Cloudy324 (25.5%)
↑7.6%prior 301
Rain157 (12.3%)
↓-13.7%prior 182
Snow58 (4.6%)
↑26.1%prior 46
Other/Unknown8 (0.6%)
↑33.3%prior 6
Fog; Smog; Smoke7 (0.5%)
↓-41.7%prior 12
Sleet; Hail6 (0.5%)
Freezing Rain or Freezing Drizzle3 (0.2%)
Blowing Sand; Soil; Dirt; Snow1 (0.1%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight817 (64.2%)
↓-6.4%prior 873
Dark - Roadway Not Lighted292 (22.9%)
↓-2.0%prior 298
Dark - Lighted Roadway106 (8.3%)
↓-10.2%prior 118
Dawn/Dusk48 (3.8%)
↓-31.4%prior 70
Other/Unknown6 (0.5%)
→0.0%prior 6
Dark - Unknown Roadway Lighting4 (0.3%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry932 (73.2%)
↓-11.3%prior 1,051
Wet261 (20.5%)
↓-3.7%prior 271
Snow40 (3.1%)
↑48.1%prior 27
Ice28 (2.2%)
↑180.0%prior 10
Slush7 (0.5%)
Other/Unknown4 (0.3%)
↓-33.3%prior 6
Water (Standing; Moving)1 (0.1%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The types of vehicles involved in crashes remained consistent between the two periods, with Chevrolet, Ford, and Honda being the top three makes in both 2023 and 2024. The number of vehicles from these top makes involved in crashes decreased, mirroring the overall reduction in collisions. The age distribution of persons involved in crashes also showed stability, with the 16-20 age group representing 11.7% of persons in 2024, down slightly from 12.5% in 2023. The 35-44 age group was the largest cohort in 2024, accounting for 368 of the 2,447 individuals involved.

Top Vehicle Makes (1,927 vehicles)

1
CHEVROLET315 (16.3%)
↓-11.5%prior 356
2
FORD269 (14%)
↓-16.2%prior 321
3
HONDA137 (7.1%)
↓-18.9%prior 169
4
TOYOTA116 (6%)
↓-22.7%prior 150
5
JEEP106 (5.5%)
↑7.1%prior 99
6
DODGE101 (5.2%)
↑16.1%prior 87
7
NISSAN87 (4.5%)
↓-11.2%prior 98
8
FREIGHTLINER70 (3.6%)
↑66.7%prior 42
9
GMC70 (3.6%)
↑7.7%prior 65
10
SUBARU69 (3.6%)
→0.0%prior 69

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (2,378 persons with recorded sex)

Male1,419 (59.7%)
↓-12.7%prior 1,626
Female959 (40.3%)
↓-12.8%prior 1,100

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 1,273
  • Total persons involved: 2,447
  • Total vehicles involved: 1,927

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: 2024." Published July 5, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/2024-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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Belmont County, OH Crash Report — 2024 | ThatCarHitMe.com