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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · 2019
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/2019-annual-report
Yearly Traffic Safety Analysis
6,606 CRASHES IN
CONNECTICUT, CT
2019
In 2019, New London County recorded 6,606 total crashes, a 2.8% decrease from the 6,797 crashes reported in 2018. Despite the overall reduction in collisions, the number of fatalities increased significantly. The most notable year-over-year change was a 45.8% rise in total fatalities, from 24 in 2018 to 35 in 2019.
6,606
▼ -2.8%was 6,797
Total Crash Events
35
▲ 45.8%was 24
Persons Killed
1,908
▲ 3.8%was 1,838
Persons Injured
784
▼ -6.4%was 838
Hit-and-Run Crashes
Note: "Persons Killed" (35) 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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, the total number of crashes in New London County decreased by 2.8% from 2018 to 2019. However, this downward trend in collisions did not extend to crash severity. Total injuries rose by 3.8% from 1,838 to 1,908, and total fatalities increased by 45.8% from 24 to 35 during the same period.
784
Hit-and-Run Crashes — 2019
▼ -6.4% vs prior (838)
The number of hit-and-run incidents in New London County decreased from 838 in 2018 to 784 in 2019, representing a 6.4% reduction. The hit-and-run rate, which measures the proportion of total crashes that were hit-and-runs, also trended downward. This rate fell from 12.3% in the prior year to 11.9% in the current year.
Vulnerable Road User Casualties
3
Pedestrians Killed
0
Cyclists Killed
32
Motorists Killed
0
Other Killed
42
Pedestrians Injured
22
Cyclists Injured
1,842
Motorists Injured
2
Other Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted between 2018 and 2019. The peak day for crashes moved from Friday (1,106 crashes) in 2018 to Tuesday (1,051 crashes) in 2019. Similarly, the peak hour for collisions shifted later in the afternoon, from 3 p.m. in the prior year (561 crashes) to 4 p.m. in the current year (639 crashes).
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes increased from 2018 to 2019, even as the total number of crashes declined. The proportion of fatal crashes rose from 0.3% to 0.4% of all incidents, and the total number of fatalities increased by 45.8%. Crashes resulting in minor injuries also increased as a proportion of the total, from 10.2% to 10.6%, while the share of non-injury crashes decreased from 79.2% to 78.6%.
Severity is per crash event (most severe injury). 29 fatal crash events resulted in 35 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record
Road & Environmental Conditions
The majority of crashes in both periods occurred in clear weather and on dry roads. In 2019, 76.8% of crashes were in clear weather, compared to 75.1% in 2018. There was a decrease in the proportion of crashes on wet road surfaces, which accounted for 15.6% of incidents in 2019, down from 17.9% in the prior year. Consequently, the share of crashes on dry roads increased from 73.9% in 2018 to 77.7% in 2019.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field
Vehicles & Demographics
The demographic profile of persons involved in crashes remained consistent year-over-year, with the 26-34 age group representing the largest share in both 2019 (17.3%) and 2018 (16.4%). The top makes of vehicles involved in collisions also showed little change. Ford, Toyota, and Honda were the most common vehicle makes in both periods, with their involvement counts remaining largely stable.
Top Vehicle Makes (11,799 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records
1,054 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (15,036 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across different speed zones was largely unchanged between 2018 and 2019, with the 25 mph zone accounting for the most incidents in both years. However, there was a notable increase in the number of fatal crashes occurring in mid-range speed zones. In 30 mph zones, fatalities increased from 2 to 7, and in 40 mph zones, fatalities rose from 0 to 4. Crashes in 45 mph zones also saw fatalities double from 4 in 2018 to 8 in 2019.
Fatal crashes by zone: 25 mph: 1 of 2,436 (0.041%) · 30 mph: 7 of 486 (1.44%) · 35 mph: 1 of 1,110 (0.09%) · 40 mph: 4 of 322 (1.242%) · 45 mph: 8 of 641 (1.248%) · 50 mph: 2 of 168 (1.19%) · 65 mph: 5 of 702 (0.712%) · 88 mph: 1 of 203 (0.493%)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
- Report generated: August 21, 2026
Data Coverage
- Reporting period: 2019-01-01 through 2019-12-31 (365 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 6,606
- Total persons involved: 15,895
- Total vehicles involved: 11,799
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: 2019." Published August 21, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2019-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: 2019-01-01 – 2019-12-31
Generated: August 21, 2026 · All rights reserved
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