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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · 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/connecticut/statewide/2023-annual-report
Yearly Traffic Safety Analysis
5,775 CRASHES IN
CONNECTICUT, CT
2023
In New London County, there were 5,775 total crashes in 2023, a 5.8% decrease from the 6,129 crashes recorded in 2022. While overall collisions declined, total injuries increased slightly from 1,870 to 1,910. The most significant year-over-year change was a 13.9% decrease in hit-and-run incidents, which fell from 799 to 688.
5,775
▼ -5.8%was 6,129
Total Crash Events
30
▲ 3.4%was 29
Persons Killed
1,910
▲ 2.1%was 1,870
Persons Injured
688
▼ -13.9%was 799
Hit-and-Run Crashes
Note: "Persons Killed" (30) counts individual fatalities across all crash events. "Fatal" in the severity table below (29) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Traffic crashes in New London County showed a downward trend from 2022 to 2023. The total number of collisions fell by 5.8%, from 6,129 to 5,775. Despite this overall decrease, total injuries rose by 2.1% (from 1,870 to 1,910), and fatalities increased slightly from 29 to 30.
688
Hit-and-Run Crashes — 2023
▼ -13.9% vs prior (799)
Hit-and-run incidents saw a notable decrease in 2023 compared to the prior year. The total number of hit-and-run crashes fell by 13.9%, from 799 in 2022 to 688 in 2023. Correspondingly, the hit-and-run rate, which measures the proportion of all crashes that are hit-and-runs, also declined from 13.0% to 11.9%.
Vulnerable Road User Casualties
3
Pedestrians Killed
1
Cyclists Killed
26
Motorists Killed
43
Pedestrians Injured
28
Cyclists Injured
1,839
Motorists Injured
Source: Connecticut Crash Data · 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 patterns of crashes remained broadly consistent year-over-year. Friday was the peak day for crashes in both 2023 (995 crashes) and 2022 (1,061 crashes). However, the peak hour shifted slightly, moving from the 3 p.m. hour in 2022 (606 crashes) to the 4 p.m. hour in 2023 (517 crashes).
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · 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 shifted slightly between the two periods. The fatal crash rate increased from 0.44% in 2022 to 0.5% in 2023, with fatal crashes rising from 27 to 29. The proportion of crashes involving minor injuries grew from 12.7% to 13.9%, while the share of crashes with no injuries decreased from 76.6% to 75.9%.
Severity is per crash event (most severe injury). 29 fatal crash events resulted in 30 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Road & Environmental Conditions
The distribution of crashes across different environmental conditions remained largely stable year-over-year, with most incidents occurring in clear weather and on dry roads in both periods. Despite an overall drop in total crashes, collisions during rain increased from 541 to 623, and those on wet roads rose from 822 to 890. Crashes in daylight decreased from 4,263 to 4,079, in line with the overall trend, while crashes in dark-lighted conditions also fell from 1,067 to 954.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes were consistent across both years: Ford, Toyota, and Honda. The number of Fords decreased from 1,273 to 1,186, while Toyotas saw an increase from 800 to 872. Regarding persons involved, the 26-34 age group remained the most frequently represented in both 2022 (2,384 persons) and 2023 (2,108 persons). The number of individuals in the 16-20 age group involved in crashes increased from 1,374 to 1,465.
Top Vehicle Makes (10,301 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
741 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (12,784 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crash distributions across speed zones shifted between 2022 and 2023. Crashes in 25 mph zones decreased from 2,222 to 1,967, although fatal crashes in this zone held steady at 5. Conversely, crashes in 65 mph zones increased from 670 to 769, while associated fatalities dropped from 6 to 3. In 2023, roads with a 40 mph speed limit saw a notable increase in fatal crashes, rising from 1 to 6, despite a similar number of total crashes in that zone (268 in 2022 vs. 265 in 2023).
Fatal crashes by zone: 1 mph: 2 of 216 (0.926%) · 25 mph: 5 of 1,967 (0.254%) · 30 mph: 1 of 437 (0.229%) · 35 mph: 5 of 900 (0.556%) · 40 mph: 6 of 265 (2.264%) · 45 mph: 3 of 599 (0.501%) · 50 mph: 1 of 171 (0.585%) · 55 mph: 2 of 160 (1.25%) · 65 mph: 3 of 769 (0.39%) · 99 mph: 1 of 8 (12.5%)
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Posted speed limit at crash location
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Connecticut Crash Data, 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: August 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 5,775
- Total persons involved: 13,576
- Total vehicles involved: 10,301
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). "connecticut, CT Crash Intelligence Report: 2023." Published August 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/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: Connecticut Crash Data · Csv
Period: 2023-01-01 – 2023-12-31
Generated: August 21, 2026 · All rights reserved
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