ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BOWLING GREEN, OH · 2023
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/ohio/bowling-green/2023-annual-report
Yearly Traffic Safety Analysis
628 CRASHES IN
BOWLING GREEN, OH
2023
Total crashes in Bowling Green remained stable year-over-year, with 628 crashes in 2023 compared to 627 crashes in 2022, representing a 0.16% increase. However, total fatalities increased by 100%, from 1 fatality in 2022 to 2 fatalities in 2023. This increase in fatalities is the most significant shift observed.
628
▲ 0.2%was 627
Total Crash Events
2
▲ 100.0%was 1
Persons Killed
177
Persons Injured
88
▲ 3.5%was 85
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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, the total number of crashes in Bowling Green remained relatively stable, with a marginal increase of 0.16% from 627 in 2022 to 628 in 2023. While total injuries remained constant at 177 for both years, fatalities doubled from 1 in 2022 to 2 in 2023. This indicates a concerning trend towards more severe outcomes despite stable crash volumes.
88
Hit-and-Run Crashes — 2023
▲ 3.5% vs prior (85)
Hit-and-run incidents showed a slight increase year-over-year, with 88 crashes reported in 2023 compared to 85 in 2022, an increase of 3.5%. The hit-and-run rate also saw a minor uptick, rising from 13.6% in 2022 to 14% in 2023.
Vulnerable Road User Casualties
0
Pedestrians Killed
2
Motorists Killed
13
Pedestrians Injured
164
Motorists Injured
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 temporal distribution of crashes showed some shifts between the two years. In 2023, Wednesday became the peak day for crashes with 102 incidents, shifting from Friday which was the peak day in 2022 with 106 crashes. The peak hour remained consistent at 5 PM for both years, though the number of crashes during this hour slightly decreased from 60 in 2022 to 58 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 severity of crashes saw notable changes year-over-year. Fatalities doubled from 1 in 2022 to 2 in 2023, increasing the fatal crash rate from 0.16% to 0.32%. While total injuries remained at 177 for both periods, serious injuries (Type A) decreased by 60% from 15 in 2022 to 6 in 2023, while minor injuries (Type B) increased by 26.5% from 68 to 86.
Outcome by Severity (Crash Events)
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 adverse conditions saw increases in 2023 compared to 2022. Crashes during rain increased by 51% (from 51 to 77), and crashes on wet road surfaces increased by 30.9% (from 97 to 127). Additionally, crashes in dark-lighted roadway conditions rose by 48.6%, from 70 in 2022 to 104 in 2023.
Weather
Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
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 increased slightly by 1.9%, from 1114 in 2022 to 1135 in 2023. A significant shift was observed in bicycle involvement, which increased by 180% from 5 in 2022 to 14 in 2023. Conversely, motorcycle involvement decreased by 44.4%, from 9 in 2022 to 5 in 2023.
Top Vehicle Makes (1,135 vehicles)
Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
70 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,340 persons with recorded sex)
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 7, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: Bowling Green, OH
- Total crash records analyzed: 628
- Total persons involved: 1,393
- Total vehicles involved: 1,135
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). "Bowling Green, OH Crash Intelligence Report: 2023." Published July 7, 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/bowling-green/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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Ohio Crash Data (ODOT TIMS) · Csv
Period: 2023-01-01 – 2023-12-31
Generated: July 7, 2026 · All rights reserved