Yearly Traffic Safety Analysis

1,063 CRASHES IN
OHIO, OH
2024

All metrics benchmarked against2023

In Knox County, total traffic crashes remained relatively stable, increasing by 1.1% from 1,051 incidents in 2023 to 1,063 in 2024. Despite the slight rise in total collisions, the most significant year-over-year change was a 57% reduction in fatalities, which fell from 7 to 3.

1,063

1.1%was 1,051

Total Crash Events

3

-57.1%was 7

Persons Killed

362

-0.3%was 363

Persons Injured

71

6.0%was 67

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 · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows a marginal increase in crash volume, with total incidents rising by 1.1% from 1,051 to 1,063 year-over-year. However, the severity of these crashes decreased, as total fatalities dropped from 7 to 3 and the number of injuries remained nearly unchanged, decreasing from 363 to 362.

71

Hit-and-Run Crashes — 2024

6.0% vs prior (67)

Hit-and-run incidents saw a slight increase in both volume and rate compared to the previous year. The total number of hit-and-run crashes rose from 67 to 71. This pushed the hit-and-run rate up slightly, from 6.4% of all crashes in the prior period to 6.7% in the current period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1-100.0%

3

Motorists Killed

Prior: 6-50.0%

8

Pedestrians Injured

Prior: 80.0%

354

Motorists Injured

Prior: 355-0.3%

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

Crash timing patterns were largely consistent between the two periods. Friday remained the day with the highest number of crashes, increasing from 177 incidents in the prior year to 196 in the current year. The daily peak for collisions shifted slightly later, from the 3 p.m. hour (83 crashes) in 2023 to the 4 p.m. hour (96 crashes) in 2024.

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 decreased in the current period compared to the prior year. Fatal crashes were reduced by half, from 6 to 3, lowering their proportion of all crashes from 0.6% to 0.3%. Correspondingly, the share of non-injury crashes increased from 74.5% to 75.8% of all incidents, while minor injury crashes fell from 13.0% to 11.9%.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.3%
-50.0%prior 6
Serious Injury30serious injury crashes2.8%
7.1%prior 28
Minor Injury127minor injury crashes11.9%
-7.3%prior 137
Possible Injury97possible injury crashes9.1%
0.0%prior 97
No Injury806no injury crashes75.8%
2.9%prior 783

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 distribution of crashes by lighting and road surface conditions was stable year-over-year, with daylight crashes comprising about 66% and dry-road crashes over 76% in both periods. A notable shift occurred in weather conditions, as the share of crashes in clear weather rose from 52.0% in 2023 to 58.9% in 2024. This was accompanied by a proportional decrease in crashes occurring under cloudy conditions.

Weather

Clear626 (58.9%)
14.4%prior 547
Cloudy273 (25.7%)
-21.8%prior 349
Rain104 (9.8%)
10.6%prior 94
Snow43 (4.0%)
13.2%prior 38
Fog; Smog; Smoke8 (0.8%)
-55.6%prior 18
Freezing Rain or Freezing Drizzle4 (0.4%)
Other/Unknown3 (0.3%)
Sleet; Hail1 (0.1%)
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

Daylight700 (65.9%)
1.4%prior 690
Dark - Roadway Not Lighted228 (21.4%)
2.2%prior 223
Dawn/Dusk79 (7.4%)
5.3%prior 75
Dark - Lighted Roadway47 (4.4%)
-11.3%prior 53
Other/Unknown6 (0.6%)
20.0%prior 5
Dark - Unknown Roadway Lighting3 (0.3%)
-40.0%prior 5

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

Road Surface

Dry814 (76.6%)
-2.5%prior 835
Wet185 (17.4%)
5.1%prior 176
Snow31 (2.9%)
24.0%prior 25
Ice24 (2.3%)
200.0%prior 8
Slush6 (0.6%)
Sand; Mud; Dirt; Oil; Gravel2 (0.2%)
Other/Unknown1 (0.1%)

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

Vehicles & Demographics

A minor change occurred in the ranking of vehicle makes involved in crashes, with Chevrolet (293 vehicles) overtaking Ford (272 vehicles) for the top position. Regarding driver demographics, the number of persons aged 65 and older involved in crashes increased significantly from 268 to 326. The 16-20 age group remained one of the most frequently involved, with 327 individuals in the current period compared to 332 in the prior period.

Top Vehicle Makes (1,693 vehicles)

1
CHEVROLET293 (17.3%)
3.9%prior 282
2
FORD272 (16.1%)
-10.5%prior 304
3
HONDA159 (9.4%)
5.3%prior 151
4
TOYOTA135 (8%)
21.6%prior 111
5
DODGE101 (6%)
6.3%prior 95
6
JEEP88 (5.2%)
-20.0%prior 110
7
NISSAN65 (3.8%)
35.4%prior 48
8
OTHER/UNKNOWN52 (3.1%)
126.1%prior 23
9
GMC51 (3%)
-5.6%prior 54
10
HYUNDAI50 (3%)
-19.4%prior 62

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

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

Sex Distribution (2,224 persons with recorded sex)

Male1,259 (56.6%)
4.0%prior 1,210
Female965 (43.4%)
3.7%prior 931

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 6, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 1,063
  • Total persons involved: 2,249
  • Total vehicles involved: 1,693

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 6, 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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Knox County, OH Crash Report — 2024 | ThatCarHitMe.com