ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · DUBLIN, 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/dublin/2023-annual-report
Yearly Traffic Safety Analysis
738 CRASHES IN
DUBLIN, OH
2023
Total crashes in Dublin increased by 13.54% year-over-year, rising from 650 incidents in the prior period to 738 in the current period. The most significant shift was a 400% increase in total fatalities, which rose from 1 to 5.
738
▲ 13.5%was 650
Total Crash Events
5
▲ 400.0%was 1
Persons Killed
313
▲ 15.1%was 272
Persons Injured
101
▲ 24.7%was 81
Hit-and-Run Crashes
Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 data indicates an upward trend year-over-year, with total crashes increasing by 13.54% from 650 to 738. This rise was accompanied by a substantial 400% increase in fatalities, climbing from 1 to 5, and a 15.07% increase in total injuries, from 272 to 313.
101
Hit-and-Run Crashes — 2023
▲ 24.7% vs prior (81)
Hit-and-run crashes increased by 20 incidents year-over-year, rising from 81 to 101. This represents a 24.69% increase in count, with the hit-and-run rate climbing from 12.5% to 13.7% of all crashes, indicating an upward trend.
Vulnerable Road User Casualties
1
Pedestrians Killed
4
Motorists Killed
7
Pedestrians Injured
306
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 peak day for crashes shifted from Wednesday in the prior period (125 crashes) to Friday in the current period (133 crashes). Similarly, the peak hour for crashes moved from 5 PM (66 crashes) to 4 PM (74 crashes) year-over-year, indicating a change in the busiest times for 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 increased significantly from 1 to 5, raising their proportion of total crashes from 0.2% to 0.7% year-over-year. While serious injury crashes slightly increased from 1.8% to 1.9% of total crashes, the overall proportion of crashes resulting in any injury (A, B, or C severity) decreased from 32.15% to 30.08%.
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 rainy weather conditions saw an increase in their proportion, rising from 9.23% to 12.47% of total crashes year-over-year. Concurrently, crashes on wet road surfaces also increased proportionally from 16.92% to 19.78%. Crashes occurring in snowy conditions decreased from 17 to 4 incidents.
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 from 1,240 to 1,420 year-over-year. Notably, semi-tractor involvement decreased from 40 to 35, and bicycle involvement dropped from 10 to 3, while motorcycle involvement increased from 4 to 12. Honda remained the most frequently involved vehicle make, increasing from 178 to 253 incidents, with Chevrolet moving from fourth to second in make rankings.
Top Vehicle Makes (1,420 vehicles)
Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
97 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,829 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 5, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: Dublin, OH
- Total crash records analyzed: 738
- Total persons involved: 1,904
- Total vehicles involved: 1,420
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). "Dublin, 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/dublin/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 5, 2026 · All rights reserved