Yearly Traffic Safety Analysis

961 CRASHES IN
OHIO, OH
2024

All metrics benchmarked against2023

In 2024, Jefferson County recorded 961 total crashes, a 3.1% increase from the 932 crashes documented in 2023. The most significant year-over-year change was a sharp rise in traffic fatalities, which increased from 1 in 2023 to 8 in 2024. This corresponded with an increase in fatal crashes from 1 to 7 over the same period.

961

3.1%was 932

Total Crash Events

8

700.0%was 1

Persons Killed

372

-1.8%was 379

Persons Injured

85

6.3%was 80

Hit-and-Run Crashes

Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) 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

Overall, traffic crashes in Jefferson County showed a slight upward trend, increasing by 29 incidents from 932 in 2023 to 961 in 2024. While total injuries saw a marginal decrease from 379 to 372, the number of fatalities increased significantly from 1 to 8 year-over-year.

85

Hit-and-Run Crashes — 2024

6.3% vs prior (80)

Hit-and-run incidents saw a slight increase in both count and rate year-over-year. In 2024, there were 85 hit-and-run crashes, up from 80 in 2023. This represents a small increase in the hit-and-run rate, which rose from 8.6% of all crashes in 2023 to 8.8% in 2024.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

8

Motorists Killed

Prior: 1700.0%

3

Pedestrians Injured

Prior: 9-66.7%

369

Motorists Injured

Prior: 370-0.3%

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 the two periods. In 2024, the peak day for crashes was Tuesday with 164 incidents, a change from 2023 when Friday was the peak day with 161 crashes. The peak hour for collisions remained consistent, occurring at 3 p.m. in both 2024 (69 crashes) and 2023 (68 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

Crash severity increased significantly in 2024, with the fatal crash rate rising to 0.73% (7 fatal crashes) from 0.11% (1 fatal crash) in 2023. While fatal incidents rose, the proportion of crashes resulting in injuries decreased across all other categories. Serious injury crashes fell slightly from 4.5% to 4.3% of all incidents, and crashes involving possible injuries dropped from 10.4% to 8.0%.

Severity is per crash event (most severe injury). 7 fatal crash events resulted in 8 persons killed.

Outcome by Severity (Crash Events)

Fatal7fatal crashes0.7%
600.0%prior 1
Serious Injury41serious injury crashes4.3%
-2.4%prior 42
Minor Injury145minor injury crashes15.1%
-2.7%prior 149
Possible Injury77possible injury crashes8%
-20.6%prior 97
No Injury691no injury crashes71.9%
7.5%prior 643

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 proportion of crashes occurring in clear and dry conditions increased in 2024 compared to the prior year. Crashes on dry roads accounted for 74.0% of the total in 2024, up from 67.9% in 2023, while crashes on wet roads decreased from 29.2% to 21.0%. Similarly, incidents in daylight rose from 64.9% to 68.4% of all crashes, and those in clear weather increased from 55.5% to 60.3%.

Weather

Clear580 (60.4%)
12.2%prior 517
Cloudy199 (20.7%)
-9.5%prior 220
Rain125 (13.0%)
-16.7%prior 150
Snow43 (4.5%)
48.3%prior 29
Fog; Smog; Smoke9 (0.9%)
50.0%prior 6
Sleet; Hail3 (0.3%)
Other/Unknown1 (0.1%)
-83.3%prior 6
Severe Crosswinds1 (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

Daylight657 (68.4%)
8.6%prior 605
Dark - Roadway Not Lighted173 (18.0%)
-8.0%prior 188
Dark - Lighted Roadway81 (8.4%)
-15.6%prior 96
Dawn/Dusk46 (4.8%)
24.3%prior 37
Dark - Unknown Roadway Lighting3 (0.3%)
-40.0%prior 5
Other/Unknown1 (0.1%)

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

Road Surface

Dry711 (74.0%)
12.3%prior 633
Wet202 (21.0%)
-25.7%prior 272
Snow33 (3.4%)
106.3%prior 16
Ice10 (1.0%)
Other/Unknown2 (0.2%)
Sand; Mud; Dirt; Oil; Gravel1 (0.1%)
Slush1 (0.1%)
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 ranking of the most common vehicle makes involved in crashes shifted, with Ford becoming the most frequent make in 2024 with 258 vehicles, up from 223 in 2023. Chevrolet, the top make in 2023 with 244 vehicles, dropped to second place with 191 vehicles in 2024. The age distribution of persons involved in crashes remained relatively stable, with no significant proportional shifts among age groups year-over-year.

Top Vehicle Makes (1,522 vehicles)

1
FORD258 (17%)
15.7%prior 223
2
CHEVROLET191 (12.5%)
-21.7%prior 244
3
HONDA139 (9.1%)
20.9%prior 115
4
TOYOTA121 (8%)
11.0%prior 109
5
JEEP82 (5.4%)
6.5%prior 77
6
DODGE73 (4.8%)
12.3%prior 65
7
HYUNDAI69 (4.5%)
53.3%prior 45
8
KIA56 (3.7%)
-6.7%prior 60
9
NISSAN55 (3.6%)
10.0%prior 50
10
GMC50 (3.3%)
19.0%prior 42

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

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

Sex Distribution (1,900 persons with recorded sex)

Male1,060 (55.8%)
5.9%prior 1,001
Female840 (44.2%)
7.4%prior 782

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: 961
  • Total persons involved: 1,945
  • Total vehicles involved: 1,522

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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Jefferson County, OH Crash Report — 2024 | ThatCarHitMe.com