Yearly Traffic Safety Analysis

11 CRASHES IN
GREEN SPRINGS, OH
2023

All metrics benchmarked against2022

In Green Springs, total crashes increased by 10% year-over-year, rising from 10 crashes in 2022 to 11 crashes in 2023. A significant change was observed in total injuries, which saw a 200% increase, going from 1 injury in 2022 to 3 injuries in 2023.

11

▲ 10.0%was 10

Total Crash Events

0

Persons Killed

3

▲ 200.0%was 1

Persons Injured

2

▲ 100.0%was 1

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, crash incidents in Green Springs showed an upward trend, with total crashes increasing by 10% from 10 in 2022 to 11 in 2023. This increase indicates a slight rise in crash occurrences compared to the previous year.

2

Hit-and-Run Crashes — 2023

▲ 100.0% vs prior (1)

Hit-and-run crashes increased from 1 in 2022 to 2 in 2023. This resulted in the hit-and-run rate rising from 10% of total crashes in 2022 to 18.2% in 2023, indicating an upward trend in such incidents.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

3

Motorists Injured

Prior: 1↑200.0%

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 Wednesday in 2022, with 3 crashes, to Tuesday in 2023, also with 3 crashes. The peak crash hour also changed, moving from 3p in 2022 (2 crashes) to 10p in 2023 (2 crashes), indicating a shift towards later evening incidents.

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 remained at zero in both 2022 and 2023. However, the total number of injured persons increased significantly from 1 in 2022 to 3 in 2023. While 2022 saw one serious injury crash (10% of total crashes), 2023 recorded one minor injury crash (9.1% of total crashes).

Outcome by Severity (Crash Events)

Minor Injury1minor injury crashes9.1%
No Injury10no injury crashes90.9%
↑11.1%prior 9

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 rainy weather increased from 1 in 2022 to 4 in 2023, while crashes during clear weather remained at 6 in both years. There was a notable shift in lighting conditions, with crashes in daylight decreasing from 8 in 2022 to 6 in 2023, and crashes in dark conditions (Dark - Roadway Not Lighted, Dark - Lighted Roadway, Dark - Unknown Roadway Lighting) increasing from 1 in 2022 to 4 in 2023.

Weather

Clear6 (54.5%)
→0.0%prior 6
Rain4 (36.4%)
Fog; Smog; Smoke1 (9.1%)

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

Lighting

Daylight6 (54.5%)
↓-25.0%prior 8
Dark - Roadway Not Lighted2 (18.2%)
Dark - Lighted Roadway1 (9.1%)
Dark - Unknown Roadway Lighting1 (9.1%)
Dawn/Dusk1 (9.1%)

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

Road Surface

Dry6 (54.5%)
↓-14.3%prior 7
Wet4 (36.4%)
Snow1 (9.1%)

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

Vehicles & Demographics

Top Vehicle Makes (17 vehicles)

1
FORD5 (29.4%)
2
CHEVROLET2 (11.8%)
3
DODGE2 (11.8%)
4
BUICK2 (11.8%)
5
HONDA1 (5.9%)
6
KIA1 (5.9%)
7
NISSAN1 (5.9%)
8
GMC1 (5.9%)

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 (15 persons with recorded sex)

Male8 (53.3%)
↓-52.9%prior 17
Female7 (46.7%)
↓-61.1%prior 18

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 5, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: Green Springs, OH
  • Total crash records analyzed: 11
  • Total persons involved: 17
  • Total vehicles involved: 17

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). "Green Springs, OH Crash Intelligence Report: 2023." Published July 5, 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/green-springs/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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Green Springs, OH Crash Report — 2023 | ThatCarHitMe.com