Yearly Traffic Safety Analysis

3,067 CRASHES IN
OHIO, OH
2023

All metrics benchmarked against2022

In 2023, Medina County recorded 3,067 total crashes, a 1.2% decrease from the 3,105 crashes documented in 2022. While overall crashes and injuries (961 vs. 1,017) declined, the number of fatalities increased from 11 to 13. The most significant year-over-year percentage change was a 150% increase in bicycle-involved crashes, which rose from 6 incidents in 2022 to 15 in 2023.

3,067

-1.2%was 3,105

Total Crash Events

13

18.2%was 11

Persons Killed

961

-5.5%was 1,017

Persons Injured

225

17.2%was 192

Hit-and-Run Crashes

Note: "Persons Killed" (13) counts individual fatalities across all crash events. "Fatal" in the severity table below (13) 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

Overall traffic crashes in Medina County showed a slight downward trend in 2023, decreasing by 1.2% from the previous year. Despite this small reduction in total collisions and a 5.5% drop in injuries, the number of fatalities increased by 18.2%, from 11 in 2022 to 13 in 2023.

225

Hit-and-Run Crashes — 2023

17.2% vs prior (192)

The number of hit-and-run crashes increased by 17.2% in 2023, rising from 192 incidents in 2022 to 225. This corresponds to an increase in the hit-and-run rate, which grew from 6.2% of all crashes in the prior year to 7.3% in the current year. The data indicates an upward trend in both the volume and proportion of hit-and-run incidents.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 10.0%

12

Motorists Killed

Prior: 1020.0%

20

Pedestrians Injured

Prior: 1717.6%

941

Motorists Injured

Prior: 1,000-5.9%

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 peak day for crashes shifted from Friday (520 crashes) in 2022 to Tuesday (509 crashes) in 2023. The afternoon commute remained the most frequent time for incidents, with the 4 p.m. hour being the peak in both periods. Crashes during this peak hour increased by 7.2% from 250 in 2022 to 268 in 2023.

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

The proportion of fatal crashes remained stable at 0.4% of all incidents in both 2022 and 2023, although the total number of fatalities increased from 11 to 13. The share of serious injury crashes grew from 3.1% of all crashes in 2022 to 3.5% in 2023. Conversely, crashes resulting in possible injuries decreased as a proportion of the total, from 8.8% to 7.5%.

Outcome by Severity (Crash Events)

Fatal13fatal crashes0.4%
18.2%prior 11
Serious Injury107serious injury crashes3.5%
12.6%prior 95
Minor Injury338minor injury crashes11%
-2.3%prior 346
Possible Injury231possible injury crashes7.5%
-15.4%prior 273
No Injury2,378no injury crashes77.5%
-0.1%prior 2,380

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 majority of crashes in both periods occurred in clear weather on dry roads. In 2023, crashes during clear weather accounted for 59.4% of the total, similar to 58.2% in 2022. There was a slight proportional increase in crashes on wet roads (from 16.2% to 18.4%) and during rainy conditions (from 7.6% to 9.9%). The proportion of crashes in dark conditions remained stable year-over-year.

Weather

Clear1,821 (59.4%)
0.8%prior 1,807
Cloudy671 (21.9%)
-9.0%prior 737
Rain303 (9.9%)
28.9%prior 235
Snow230 (7.5%)
-11.2%prior 259
Fog; Smog; Smoke22 (0.7%)
-24.1%prior 29
Sleet; Hail7 (0.2%)
0.0%prior 7
Other/Unknown5 (0.2%)
Severe Crosswinds3 (0.1%)
-50.0%prior 6
Freezing Rain or Freezing Drizzle3 (0.1%)
-62.5%prior 8
Blowing Sand; Soil; Dirt; Snow2 (0.1%)
-85.7%prior 14

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

Lighting

Daylight2,011 (65.6%)
-2.6%prior 2,065
Dark - Roadway Not Lighted512 (16.7%)
-2.7%prior 526
Dark - Lighted Roadway326 (10.6%)
4.5%prior 312
Dawn/Dusk196 (6.4%)
1.0%prior 194
Dark - Unknown Roadway Lighting16 (0.5%)
166.7%prior 6
Other/Unknown6 (0.2%)

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

Road Surface

Dry2,268 (73.9%)
-1.9%prior 2,312
Wet564 (18.4%)
12.1%prior 503
Snow174 (5.7%)
-10.8%prior 195
Ice40 (1.3%)
-42.0%prior 69
Other/Unknown6 (0.2%)
Water (Standing; Moving)6 (0.2%)
Slush5 (0.2%)
-75.0%prior 20
Sand; Mud; Dirt; Oil; Gravel4 (0.1%)

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

Vehicles & Demographics

Passenger Cars and Sport Utility Vehicles were the most common vehicle types involved in crashes for both years. Ford (767) and Chevrolet (608) were the top two vehicle makes involved in crashes in 2023, maintaining their rankings from 2022. Among persons involved in crashes, the 65+ age group saw an increase from 921 to 969 individuals, while the 26-34 age group saw a decrease from 1,015 to 917.

Top Vehicle Makes (5,283 vehicles)

1
FORD767 (14.5%)
4.9%prior 731
2
CHEVROLET608 (11.5%)
-8.0%prior 661
3
OTHER/UNKNOWN444 (8.4%)
13.3%prior 392
4
TOYOTA383 (7.2%)
-13.0%prior 440
5
HONDA374 (7.1%)
-0.5%prior 376
6
JEEP264 (5%)
6.0%prior 249
7
KIA253 (4.8%)
1.6%prior 249
8
NISSAN201 (3.8%)
-1.5%prior 204
9
DODGE180 (3.4%)
-24.1%prior 237
10
HYUNDAI179 (3.4%)
-10.5%prior 200

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

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

Sex Distribution (6,984 persons with recorded sex)

Male3,840 (55.0%)
-2.9%prior 3,955
Female3,144 (45.0%)
-3.6%prior 3,262

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 6, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 3,067
  • Total persons involved: 7,089
  • Total vehicles involved: 5,283

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 6, 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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Medina County, OH Crash Report — 2023 | ThatCarHitMe.com