Yearly Traffic Safety Analysis

1,247 CRASHES IN
GROVE CITY, OH
2025

All metrics benchmarked against2024

In 2025, Grove City experienced a total of 1247 crashes, marking a 13.47% increase from the 1099 crashes reported in 2024. The most significant year-over-year shift was an 80% decrease in total fatalities, falling from 5 in 2024 to 1 in 2025.

1,247

13.5%was 1,099

Total Crash Events

1

-80.0%was 5

Persons Killed

487

13.5%was 429

Persons Injured

227

29.7%was 175

Hit-and-Run Crashes

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash incidents in Grove City increased by 13.47% year-over-year, with 1247 crashes in 2025 compared to 1099 in 2024. Despite this rise in total crashes, total fatalities significantly decreased by 80%, from 5 in 2024 to 1 in 2025. Concurrently, the number of injured persons also saw an increase of 13.52%, rising from 429 to 487.

227

Hit-and-Run Crashes — 2025

29.7% vs prior (175)

Hit-and-run crashes increased by 29.71% year-over-year, rising from 175 incidents in 2024 to 227 in 2025. This increase also led to a rise in the hit-and-run rate, which climbed from 15.9% of all crashes in 2024 to 18.2% in 2025, indicating an upward trend.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

1

Motorists Killed

Prior: 5-80.0%

9

Pedestrians Injured

Prior: 13-30.8%

478

Motorists Injured

Prior: 41614.9%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal distribution of crashes in Grove City remained consistent year-over-year, with Friday continuing to be the peak day for crashes, increasing from 187 in 2024 to 217 in 2025. Similarly, 4 p.m. remained the peak hour for crash occurrences in both periods, rising from 117 crashes in 2024 to 127 crashes in 2025. Overall, crash counts increased across most days and hours compared to the prior year.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes significantly decreased by 80% year-over-year, dropping from 5 in 2024 to 1 in 2025, resulting in a lower fatal crash rate of 0.1% compared to 0.5% previously. While serious injury crashes increased from 17 to 31, their proportion of total crashes rose from 1.5% to 2.5%. Minor and possible injury crashes also saw increases in counts, from 154 to 160 and 127 to 146 respectively, with their overall proportions remaining relatively stable.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.1%
-80.0%prior 5
Serious Injury31serious injury crashes2.5%
82.4%prior 17
Minor Injury160minor injury crashes12.8%
3.9%prior 154
Possible Injury146possible injury crashes11.7%
15.0%prior 127
No Injury909no injury crashes72.9%
14.2%prior 796

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes occurring in snowy weather conditions increased significantly, rising from 14 in 2024 to 43 in 2025, with its proportion of total crashes increasing from 1.3% to 3.5%. This is reflected in road surface conditions, where crashes on snowy surfaces increased from 9 to 41, and on icy surfaces from 3 to 9. The proportion of crashes occurring in daylight remained stable at around 70%, while crashes in dark, unlighted conditions increased from 66 to 92, and their proportion rose from 6.0% to 7.4%.

Weather

Clear783 (62.8%)
15.0%prior 681
Cloudy267 (21.4%)
3.5%prior 258
Rain149 (11.9%)
16.4%prior 128
Snow43 (3.4%)
207.1%prior 14
Other/Unknown2 (0.2%)
-83.3%prior 12
Sleet; Hail2 (0.2%)
Fog; Smog; Smoke1 (0.1%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight883 (70.8%)
14.1%prior 774
Dark - Lighted Roadway204 (16.4%)
12.7%prior 181
Dark - Roadway Not Lighted92 (7.4%)
39.4%prior 66
Dawn/Dusk60 (4.8%)
-6.3%prior 64
Dark - Unknown Roadway Lighting4 (0.3%)
Other/Unknown4 (0.3%)
-63.6%prior 11

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry972 (77.9%)
14.6%prior 848
Wet223 (17.9%)
-1.3%prior 226
Snow41 (3.3%)
355.6%prior 9
Ice9 (0.7%)
Other/Unknown2 (0.2%)
-81.8%prior 11

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 2079 in 2024 to 2355 in 2025. While Passenger Cars, SUVs, and Pickups remained the most frequently involved vehicle types, the ranking of top makes shifted, with Chevrolet rising to the top spot with 312 vehicles, surpassing Honda which decreased from 287 to 277. All age groups, except for 16-20 year olds, saw an increase in the number of persons involved in crashes, with the 0-15 and 65+ age groups showing notable increases.

Top Vehicle Makes (2,355 vehicles)

1
CHEVROLET312 (13.2%)
24.8%prior 250
2
FORD281 (11.9%)
4.5%prior 269
3
HONDA277 (11.8%)
-3.5%prior 287
4
TOYOTA229 (9.7%)
23.8%prior 185
5
HYUNDAI124 (5.3%)
20.4%prior 103
6
KIA94 (4%)
-1.1%prior 95
7
JEEP94 (4%)
30.6%prior 72
8
NISSAN91 (3.9%)
-15.0%prior 107
9
DODGE77 (3.3%)
30.5%prior 59
10
GMC55 (2.3%)
27.9%prior 43

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (2,914 persons with recorded sex)

Male1,619 (55.6%)
12.1%prior 1,444
Female1,295 (44.4%)
6.8%prior 1,213

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: Grove City, OH
  • Total crash records analyzed: 1,247
  • Total persons involved: 3,079
  • Total vehicles involved: 2,355

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). "Grove City, OH Crash Intelligence Report: 2025." Published July 5, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/grove-city/2025-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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Grove City, OH Crash Report — 2025 | ThatCarHitMe.com