Yearly Traffic Safety Analysis

64 CRASHES IN
BAY VILLAGE, OH
2023

All metrics benchmarked against2022

In 2023, Bay Village experienced 64 crashes, a decrease from 82 crashes in 2022, representing a 21.95% reduction. A notable shift was the absence of fatalities in 2023, compared to one fatality in 2022, alongside a significant drop in DUI-related crashes.

64

▼ -22.0%was 82

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

23

▼ -37.8%was 37

Persons Injured

5

▲ 66.7%was 3

Hit-and-Run Crashes

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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, crashes in Bay Village decreased by 21.95% year-over-year, from 82 crashes in 2022 to 64 crashes in 2023. This decline was accompanied by a 37.84% reduction in total injuries, falling from 37 in 2022 to 23 in 2023.

5

Hit-and-Run Crashes — 2023

▲ 66.7% vs prior (3)

Hit-and-run crashes increased from 3 incidents in 2022 to 5 incidents in 2023. Consequently, the hit-and-run rate rose from 3.7% in 2022 to 7.8% in 2023.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 1↓-100.0%

5

Pedestrians Injured

Prior: 2↑150.0%

18

Motorists Injured

Prior: 35↓-48.6%

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 remained Thursday for both years, though the count decreased from 16 in 2022 to 13 in 2023. The peak hour for crashes shifted from 3 PM in 2022, which saw 12 crashes, to 5 PM in 2023, with 8 crashes.

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

Fatal crashes decreased from 1 in 2022 to 0 in 2023, and serious injury crashes also fell from 3 to 0 over the same period. The proportion of crashes resulting in no injury increased from 64.6% in 2022 to 70.3% in 2023, while minor injury crashes decreased from 19 to 10.

Outcome by Severity (Crash Events)

Minor Injury10minor injury crashes15.6%
↓-47.4%prior 19
Possible Injury9possible injury crashes14.1%
↑50.0%prior 6
No Injury45no injury crashes70.3%
↓-15.1%prior 53

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

Crashes occurring in clear weather conditions remained the most frequent, with 45 in 2023 and 46 in 2022. Notably, crashes during cloudy conditions decreased from 23 in 2022 to 10 in 2023, and wet road surface crashes also saw a reduction from 18 to 8.

Weather

Clear45 (70.3%)
↓-2.2%prior 46
Cloudy10 (15.6%)
↓-56.5%prior 23
Snow5 (7.8%)
Rain3 (4.7%)
↓-62.5%prior 8
Fog; Smog; Smoke1 (1.6%)

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

Lighting

Daylight46 (71.9%)
↓-28.1%prior 64
Dark - Lighted Roadway14 (21.9%)
→0.0%prior 14
Dawn/Dusk3 (4.7%)
Dark - Roadway Not Lighted1 (1.6%)

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

Road Surface

Dry53 (82.8%)
↓-14.5%prior 62
Wet8 (12.5%)
↓-55.6%prior 18
Snow3 (4.7%)

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

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 165 in 2022 to 125 in 2023. While passenger cars and pick-up trucks saw decreases in involvement, pedestrian/skater involvement increased from 2 to 5, and motorcycle involvement rose from 0 to 1. The age groups 0-15, 16-20, 21-25, 26-34, 55-64, and 65+ all saw fewer persons involved in crashes year-over-year.

Top Vehicle Makes (125 vehicles)

1
HONDA17 (13.6%)
→0.0%prior 17
2
FORD15 (12%)
↓-31.8%prior 22
3
TOYOTA13 (10.4%)
↑18.2%prior 11
4
CHEVROLET9 (7.2%)
↓-50.0%prior 18
5
KIA6 (4.8%)
↓-40.0%prior 10
6
JEEP6 (4.8%)
↓-14.3%prior 7
7
VOLKSWAGEN5 (4%)
→0.0%prior 5
8
GMC5 (4%)
↓-44.4%prior 9
9
MERCEDES-BENZ5 (4%)
10
SUBARU5 (4%)
→0.0%prior 5

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

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

Sex Distribution (165 persons with recorded sex)

Male84 (50.9%)
↓-28.2%prior 117
Female81 (49.1%)
↓-22.1%prior 104

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: Bay Village, OH
  • Total crash records analyzed: 64
  • Total persons involved: 167
  • Total vehicles involved: 125

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). "Bay Village, 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/bay-village/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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Bay Village, OH Crash Report — 2023 | ThatCarHitMe.com