Yearly Traffic Safety Analysis

68 CRASHES IN
COAL GROVE, OH
2022

All metrics benchmarked against2021

Total crashes in Coal Grove decreased by 6.85% from 73 in 2021 to 68 in 2022. This period saw a notable decrease in crashes involving driving under the influence, dropping from 6 incidents in 2021 to 1 in 2022.

68

-6.8%was 73

Total Crash Events

0

Persons Killed

28

-12.5%was 32

Persons Injured

8

33.3%was 6

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 · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash activity in Coal Grove showed a downward trend year-over-year. Total crashes decreased by 6.85%, from 73 in 2021 to 68 in 2022, while total injuries also declined by 12.5%, from 32 to 28.

8

Hit-and-Run Crashes — 2022

33.3% vs prior (6)

Hit-and-run crashes increased from 6 incidents in 2021 to 8 incidents in 2022. This resulted in an increase in the hit-and-run rate from 8.2% in 2021 to 11.8% in 2022, indicating an upward trend for these types of incidents.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

28

Motorists Injured

Prior: 32-12.5%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · 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 in both periods, though the number of crashes on Thursdays decreased from 22 in 2021 to 15 in 2022. The peak hour shifted from 5 PM with 14 crashes in 2021 to 3 PM with 9 crashes in 2022.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at zero in both 2021 and 2022. Serious injury crashes (Severity A) decreased from 3 (4.1% of total crashes) in 2021 to 1 (1.5%) in 2022, while possible injury crashes (Severity C) also decreased from 11 (15.1%) to 7 (10.3%). Conversely, minor injury crashes (Severity B) increased from 7 (9.6%) in 2021 to 9 (13.2%) in 2022.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.5%
-66.7%prior 3
Minor Injury9minor injury crashes13.2%
28.6%prior 7
Possible Injury7possible injury crashes10.3%
-36.4%prior 11
No Injury51no injury crashes75%
-1.9%prior 52

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes occurring in clear weather conditions increased from 37 in 2021 to 53 in 2022, while those in cloudy conditions decreased from 25 to 6. Crashes on wet road surfaces decreased from 14 in 2021 to 8 in 2022. Crashes in daylight decreased from 59 to 51, while crashes in dark-lighted conditions remained stable at 9.

Weather

Clear53 (77.9%)
43.2%prior 37
Rain7 (10.3%)
-30.0%prior 10
Cloudy6 (8.8%)
-76.0%prior 25
Snow2 (2.9%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight51 (75.0%)
-13.6%prior 59
Dark - Lighted Roadway9 (13.2%)
0.0%prior 9
Dawn/Dusk4 (5.9%)
Dark - Roadway Not Lighted3 (4.4%)
Dark - Unknown Roadway Lighting1 (1.5%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry59 (86.8%)
1.7%prior 58
Wet8 (11.8%)
-42.9%prior 14
Snow1 (1.5%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 142 in 2021 to 135 in 2022. Passenger cars, SUVs, and pickup trucks all saw a decrease in involvement. Ford remained the most common vehicle make, with Chevrolet increasing its representation from 22 to 24 vehicles, while Nissan and Honda entered the top 5 makes in 2022.

Top Vehicle Makes (135 vehicles)

1
FORD30 (22.2%)
-6.3%prior 32
2
CHEVROLET24 (17.8%)
9.1%prior 22
3
NISSAN11 (8.1%)
83.3%prior 6
4
HONDA11 (8.1%)
57.1%prior 7
5
TOYOTA10 (7.4%)
66.7%prior 6
6
DODGE8 (5.9%)
-11.1%prior 9
7
GMC6 (4.4%)
8
CHRYSLER5 (3.7%)
-37.5%prior 8
9
KIA5 (3.7%)
-28.6%prior 7
10
JEEP4 (3%)
-33.3%prior 6

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

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

Sex Distribution (184 persons with recorded sex)

Female95 (51.6%)
-10.4%prior 106
Male89 (48.4%)
17.1%prior 76

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
  • Report generated: July 6, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: Coal Grove, OH
  • Total crash records analyzed: 68
  • Total persons involved: 191
  • Total vehicles involved: 135

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). "Coal Grove, OH Crash Intelligence Report: 2022." Published July 6, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/coal-grove/2022-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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Coal Grove, OH Crash Report — 2022 | ThatCarHitMe.com