Yearly Traffic Safety Analysis

1,366 CRASHES IN
OHIO, OH
2023

All metrics benchmarked against2022

In Belmont County, total traffic crashes decreased by 7.1% from 1,470 in 2022 to 1,366 in 2023. While overall collisions and fatalities declined, the most notable shift was an increase in the number of serious injury crashes, which rose from 24 in the prior year to 31 in the current period. This occurred despite a drop in total persons injured.

1,366

-7.1%was 1,470

Total Crash Events

3

-25.0%was 4

Persons Killed

444

0.9%was 440

Persons Injured

127

-14.8%was 149

Hit-and-Run Crashes

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety data indicates a downward trend in the overall number of crashes in Belmont County, with 104 fewer incidents in 2023 compared to 2022, representing a 7.1% decrease. Fatalities also saw a slight decline from 4 to 3. However, the total number of injuries remained stable, increasing marginally from 440 to 444.

127

Hit-and-Run Crashes — 2023

-14.8% vs prior (149)

Hit-and-run incidents in Belmont County showed a downward trend. The total number of hit-and-run crashes fell from 149 in 2022 to 127 in 2023. Correspondingly, the hit-and-run rate, or the percentage of all crashes classified as such, decreased from 10.1% to 9.3% year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1-100.0%

3

Motorists Killed

Prior: 30.0%

5

Pedestrians Injured

Prior: 366.7%

439

Motorists Injured

Prior: 4370.5%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained largely consistent year-over-year. Friday was the peak day for crashes in both 2023 (230 crashes) and 2022 (239 crashes). The peak hour for collisions shifted slightly later, from the 3 PM hour in 2022 (116 crashes) to the 4 PM hour in 2023 (108 crashes), with both periods showing a concentration of incidents during the afternoon commute.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of crashes decreased, the severity of injury-related incidents shifted. The fatal crash rate declined from 0.27% to 0.22% year-over-year. However, the proportion of crashes involving serious injuries increased from 1.6% (24 incidents) in 2022 to 2.3% (31 incidents) in 2023. Concurrently, the share of non-injury crashes fell from 77.8% to 75.7% of all incidents.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.2%
-25.0%prior 4
Serious Injury31serious injury crashes2.3%
29.2%prior 24
Minor Injury193minor injury crashes14.1%
11.6%prior 173
Possible Injury105possible injury crashes7.7%
-16.7%prior 126
No Injury1,034no injury crashes75.7%
-9.5%prior 1,143

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The distribution of crashes by environmental conditions saw a notable shift related to road surface. The proportion of crashes occurring on roads with snow or ice decreased significantly, from 9.0% of all incidents in 2022 to just 2.7% in 2023. In contrast, the percentage of crashes on wet roads increased from 16.8% to 19.8%. Lighting conditions remained consistent, with daylight crashes accounting for approximately 64% of incidents in both years.

Weather

Clear817 (59.8%)
-4.3%prior 854
Cloudy301 (22.0%)
-13.0%prior 346
Rain182 (13.3%)
13.8%prior 160
Snow46 (3.4%)
-48.3%prior 89
Fog; Smog; Smoke12 (0.9%)
33.3%prior 9
Other/Unknown6 (0.4%)
Freezing Rain or Freezing Drizzle1 (0.1%)
Sleet; Hail1 (0.1%)
-80.0%prior 5

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight873 (63.9%)
-6.4%prior 933
Dark - Roadway Not Lighted298 (21.8%)
-3.9%prior 310
Dark - Lighted Roadway118 (8.6%)
-23.4%prior 154
Dawn/Dusk70 (5.1%)
4.5%prior 67
Other/Unknown6 (0.4%)
Dark - Unknown Roadway Lighting1 (0.1%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry1,051 (76.9%)
-2.9%prior 1,082
Wet271 (19.8%)
9.7%prior 247
Snow27 (2.0%)
-71.0%prior 93
Ice10 (0.7%)
-75.0%prior 40
Other/Unknown6 (0.4%)
Slush1 (0.1%)
-80.0%prior 5

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

Passenger Cars, Sport Utility Vehicles, and Pick-ups were the three most frequently involved vehicle types in both 2023 and 2022. While Chevrolet and Ford remained the top two makes by crash involvement, both saw their numbers decrease from the prior year. An analysis of persons involved in crashes shows a proportional increase in the 65+ age group, which accounted for 13.8% of individuals in 2023, up from 12.4% in 2022.

Top Vehicle Makes (2,120 vehicles)

1
CHEVROLET356 (16.8%)
-11.4%prior 402
2
FORD321 (15.1%)
-10.6%prior 359
3
HONDA169 (8%)
17.4%prior 144
4
TOYOTA150 (7.1%)
17.2%prior 128
5
JEEP99 (4.7%)
-11.6%prior 112
6
NISSAN98 (4.6%)
2.1%prior 96
7
DODGE87 (4.1%)
-29.8%prior 124
8
KIA84 (4%)
7.7%prior 78
9
SUBARU69 (3.3%)
4.5%prior 66
10
GMC65 (3.1%)
-15.6%prior 77

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (2,726 persons with recorded sex)

Male1,626 (59.6%)
-8.3%prior 1,773
Female1,100 (40.4%)
2.3%prior 1,075

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 1,366
  • Total persons involved: 2,793
  • Total vehicles involved: 2,120

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: 2023." Published July 5, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/2023-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 — 2023 | ThatCarHitMe.com