Yearly Traffic Safety Analysis

963 CRASHES IN
OHIO, OH
2024

All metrics benchmarked against2023

In Darke County, total vehicle crashes decreased by 7.5% from 1,041 in 2023 to 963 in 2024. Despite the overall reduction in collisions, the most notable year-over-year shift was a 60% decrease in traffic fatalities, which fell from 10 in the prior period to 4 in the current period. Conversely, the total number of injuries saw a slight increase of 3.6%, from 334 to 346.

963

▼ -7.5%was 1,041

Total Crash Events

4

▼ -60.0%was 10

Persons Killed

346

▲ 3.6%was 334

Persons Injured

104

▲ 26.8%was 82

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 Darke County is a reduction in traffic collisions year-over-year. Total crashes fell from 1,041 to 963, and critically, traffic fatalities decreased from 10 to 4. However, the number of people injured in these incidents ticked up slightly from 334 to 346.

104

Hit-and-Run Crashes — 2024

▲ 26.8% vs prior (82)

Hit-and-run incidents increased in both absolute numbers and as a percentage of total crashes. The count of hit-and-run crashes rose from 82 in 2023 to 104 in 2024. Consequently, the hit-and-run rate trended upward, increasing from 7.9% to 10.8% of all collisions year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 10↓-60.0%

6

Pedestrians Injured

Prior: 5↑20.0%

340

Motorists Injured

Prior: 329↑3.3%

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 timing of crashes showed some consistency and some minor shifts between the two periods. The peak hour for crashes remained 3 p.m. in both 2024 (80 crashes) and 2023 (89 crashes). The most common day for crashes shifted slightly from Thursday (178 crashes) in the prior period to Friday (179 crashes) in the current period.

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 improved significantly year-over-year. Fatal crashes dropped from 9 to 4, representing a decrease in the fatal crash rate from 0.9% to 0.4% of all incidents. While the proportion of serious injury crashes remained stable at approximately 4%, crashes resulting in minor injuries increased from 11.9% (124 incidents) to 14.6% (141 incidents) of the total.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.4%
↓-55.6%prior 9
Serious Injury39serious injury crashes4%
↓-4.9%prior 41
Minor Injury141minor injury crashes14.6%
↑13.7%prior 124
Possible Injury54possible injury crashes5.6%
↓-1.8%prior 55
No Injury725no injury crashes75.3%
↓-10.7%prior 812

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

Crash conditions were broadly similar year-over-year, with most incidents in both periods occurring in clear weather on dry roads. There was a notable shift in lighting conditions, with the proportion of crashes in daylight increasing from 52.1% (542 crashes) in 2023 to 59.9% (577 crashes) in 2024. Concurrently, crashes in dark, unlighted conditions decreased from 354 incidents to 280.

Weather

Clear679 (70.5%)
↓-11.2%prior 765
Cloudy142 (14.7%)
↓-0.7%prior 143
Rain66 (6.9%)
↓-14.3%prior 77
Snow50 (5.2%)
↑92.3%prior 26
Other/Unknown14 (1.5%)
↑40.0%prior 10
Fog; Smog; Smoke6 (0.6%)
↓-45.5%prior 11
Blowing Sand; Soil; Dirt; Snow3 (0.3%)
Freezing Rain or Freezing Drizzle1 (0.1%)
Severe Crosswinds1 (0.1%)
Sleet; Hail1 (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

Daylight577 (59.9%)
↑6.5%prior 542
Dark - Roadway Not Lighted280 (29.1%)
↓-20.9%prior 354
Dawn/Dusk53 (5.5%)
↓-23.2%prior 69
Dark - Lighted Roadway43 (4.5%)
↓-33.8%prior 65
Other/Unknown8 (0.8%)
↓-11.1%prior 9
Dark - Unknown Roadway Lighting2 (0.2%)

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

Road Surface

Dry751 (78.0%)
↓-12.5%prior 858
Wet129 (13.4%)
↓-3.0%prior 133
Snow64 (6.6%)
↑190.9%prior 22
Ice12 (1.2%)
↓-42.9%prior 21
Other/Unknown7 (0.7%)
↑40.0%prior 5

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet (288 vehicles) and Ford (269 vehicles) leading in 2024. While Chevrolet's involvement decreased from 327 in the prior year, Ford's involvement increased from 228. Regarding persons involved, the primary age groups remained 16-20, 35-44, and 65+, with the 16-20 age group seeing a slight increase in representation from 240 to 255 individuals.

Top Vehicle Makes (1,499 vehicles)

1
CHEVROLET288 (19.2%)
↓-11.9%prior 327
2
FORD269 (17.9%)
↑18.0%prior 228
3
HONDA127 (8.5%)
↑23.3%prior 103
4
DODGE98 (6.5%)
↓-6.7%prior 105
5
GMC68 (4.5%)
↓-16.0%prior 81
6
TOYOTA57 (3.8%)
↓-20.8%prior 72
7
JEEP56 (3.7%)
↑24.4%prior 45
8
NISSAN51 (3.4%)
↑13.3%prior 45
9
BUICK50 (3.3%)
↑16.3%prior 43
10
CHRYSLER42 (2.8%)
→0.0%prior 42

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

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

Sex Distribution (1,654 persons with recorded sex)

Male977 (59.1%)
↓-2.6%prior 1,003
Female677 (40.9%)
↓-4.6%prior 710

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: 963
  • Total persons involved: 1,736
  • Total vehicles involved: 1,499

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