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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
29,298 CRASHES IN
CONNECTICUT, CT
2023
In Fairfield County, total traffic crashes decreased by 4.0% from 30,519 in 2022 to 29,298 in 2023. While overall crashes and injuries declined, the most notable year-over-year shift was a 15.6% increase in crashes involving a driver under the influence (DUI), which rose from 552 to 638 incidents.
29,298
▼ -4.0%was 30,519
Total Crash Events
60
▲ 5.3%was 57
Persons Killed
8,674
▼ -4.1%was 9,042
Persons Injured
3,382
▼ -5.7%was 3,586
Hit-and-Run Crashes
Note: "Persons Killed" (60) counts individual fatalities across all crash events. "Fatal" in the severity table below (55) 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
The overall trend for traffic incidents in Fairfield County shows a modest year-over-year improvement, with total crashes falling by 4.0% and total injuries decreasing by 4.1%. In contrast to this downward trend, the number of fatalities resulting from crashes saw a slight increase, rising from 57 in 2022 to 60 in 2023.
3,382
Hit-and-Run Crashes — 2023
▼ -5.7% vs prior (3,586)
The number of hit-and-run crashes in Fairfield County decreased from 3,586 in 2022 to 3,382 in 2023, representing a 5.7% reduction. This decline outpaced the overall drop in total crashes. As a result, the hit-and-run rate, or the proportion of all crashes that were hit-and-runs, trended down slightly from 11.8% to 11.5%.
Vulnerable Road User Casualties
16
Pedestrians Killed
0
Cyclists Killed
44
Motorists Killed
338
Pedestrians Injured
86
Cyclists Injured
8,250
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 highly consistent between the two periods. Friday was the day with the most crashes in both 2022 (5,122) and 2023 (4,646). Similarly, the 5 PM hour was the peak time for collisions in both years, accounting for 2,522 crashes in 2022 and 2,452 in 2023, with the lower volume reflecting the overall decrease in 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 distribution of crash severity was largely stable year-over-year, with the fatal crash rate experiencing a minor increase from 0.18% to 0.19%. Crashes resulting in no injuries accounted for approximately 78% of all incidents in both 2022 and 2023. The number of serious injury crashes decreased from 309 to 263, though the overall proportion of crashes involving any type of injury remained steady at around 22%.
Severity is per crash event (most severe injury). 55 fatal crash events resulted in 60 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
Environmental conditions at the time of crashes were broadly similar in 2023 compared to 2022, with the majority of incidents occurring in daylight and on dry roads in both years. There was a slight shift toward more crashes in adverse weather, as the proportion of collisions on wet road surfaces increased from 13.2% to 15.2% year-over-year. Correspondingly, crashes during rainy conditions also saw a small proportional increase from 8.9% to 10.4% of the total.
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 vehicle makes most frequently involved in collisions remained consistent, with Honda, Toyota, and Ford ranking as the top three in both 2023 and 2022. The demographic profile of persons involved in crashes also showed little change between the two periods. The 26-34 age group was the largest cohort of people involved in crashes in both years, representing 16.4% of persons in 2022 and 16.6% in 2023.
Top Vehicle Makes (56,768 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
4,639 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (64,488 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
The distribution of crashes across speed zones largely mirrored the overall decline in collisions, with fewer incidents recorded in the most common 25 mph and 55 mph zones in 2023. However, the number of fatalities in 40 mph zones increased from 8 to 10, and the fatal crash rate for that zone rose from 0.74% to 0.94%. Conversely, fatal crashes in 25 mph zones decreased from 18 to 16.
Fatal crashes by zone: 1 mph: 2 of 5,899 (0.034%) · 5 mph: 1 of 42 (2.381%) · 25 mph: 16 of 9,739 (0.164%) · 30 mph: 8 of 1,793 (0.446%) · 35 mph: 3 of 1,874 (0.16%) · 40 mph: 10 of 1,063 (0.941%) · 45 mph: 2 of 198 (1.01%) · 55 mph: 12 of 5,189 (0.231%) · 65 mph: 1 of 391 (0.256%)
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 20, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 29,298
- Total persons involved: 69,830
- Total vehicles involved: 56,768
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 20, 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 20, 2026 · All rights reserved
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