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ThatCarHitMe.com
An Injuria.ai Company
CRASH INTELLIGENCE 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
112,611 CRASHES IN
CONNECTICUT, CT
2019
In 2019, Connecticut recorded 112,611 traffic crashes, resulting in 256 fatalities and 37,323 injuries. A notable statistical finding is the concentration of crashes during specific times, with Fridays being the most frequent day for incidents and the 5 p.m. hour representing the daily peak.
112,611
Total Crash Events
256
Persons Killed
37,323
Persons Injured
11.2%
Hit-and-Run Rate
Note: "Persons Killed" (256) counts individual fatalities across all crash events. "Fatal" in the severity table below (238) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records
12,668
Hit-and-Run Crashes — 2019
A total of 12,668 crashes, or 11.2% of all incidents, were classified as hit-and-run. This designation is based on the responding officer's initial determination at the scene of the collision.
Vulnerable Road User Casualties
Motor vehicle occupants constituted the largest group of casualties, with 197 motorists killed and 35,517 injured. Vulnerable road users also faced significant risk, with 56 pedestrians killed and 1,382 injured. Additionally, 3 cyclists were killed and 413 were injured in traffic crashes.
56
Pedestrians Killed
3
Cyclists Killed
197
Motorists Killed
0
Other Killed
1,382
Pedestrians Injured
413
Cyclists Injured
35,517
Motorists Injured
11
Other Injured
Source: Connecticut Crash Data Repository · 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
Crash patterns show distinct peaks in the afternoon and on the end of the work week. The most crashes occurred on Fridays (18,856) and during the 5 p.m. hour (9,852), aligning with the evening commute. A majority of crashes, 70.1% (78,949), happened during daylight hours.
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The vast majority of crashes, 75.7% (85,245), resulted in no injuries and were property-damage-only. Approximately 24.1% of crashes involved a possible, minor, or serious injury. A total of 238 crashes were fatal, which is distinct from the total of 256 persons killed in those incidents.
Severity is per crash event (most severe injury). 238 fatal crash events resulted in 256 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record
Road & Environmental Conditions
The majority of crashes occurred in ideal driving conditions. Data shows that 78.9% of crashes (88,865) happened in clear weather, 78.5% (88,366) on dry road surfaces, and 70.1% (78,949) in daylight. For comparison, 12,167 crashes occurred during rain and 17,855 on wet roads.
Weather
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field
Vehicles & Demographics
Among the 278,517 people involved in crashes, the 26-34 age group was the most represented, with 47,913 individuals. The vehicle makes most frequently involved in collisions were Honda (22,512), Toyota (21,069), and Ford (19,434). These figures represent crash involvement and do not imply fault or reflect vehicle market share.
Top Vehicle Makes (213,439 vehicles)
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records
17,460 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (261,601 persons with recorded sex)
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events
Speed Limit Zones
The 25 mph speed zone saw the highest number of crashes, with 34,785 incidents, accounting for 30.9% of the total. While lower speed zones had more crashes, the percentage of crashes that proved fatal tended to increase with the posted speed limit. For example, 0.13% of crashes in 25 mph zones were fatal, whereas that figure rises to 0.80% in 45 mph zones and 0.42% in 65 mph zones.
Fatal crashes by zone: 1 mph: 8 of 13,288 (0.06%) · 20 mph: 2 of 558 (0.358%) · 25 mph: 46 of 34,785 (0.132%) · 30 mph: 31 of 9,044 (0.343%) · 35 mph: 23 of 12,919 (0.178%) · 40 mph: 27 of 6,630 (0.407%) · 45 mph: 33 of 4,145 (0.796%) · 50 mph: 11 of 3,078 (0.357%) · 55 mph: 16 of 10,075 (0.159%) · 65 mph: 26 of 6,160 (0.422%) · 88 mph: 9 of 6,943 (0.13%) · 99 mph: 1 of 869 (0.115%)
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Posted speed limit at crash location
Top Counties
Crash distribution was heavily concentrated in three counties, which together accounted for over 82% of all incidents in the state. Fairfield County had the most crashes with 33,043, followed by New Haven County with 31,594 and Hartford County with 28,710.
Top Counties
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash-level records
Top Towns
The state's most populous cities saw the highest number of traffic incidents. New Haven recorded the most crashes with 7,556, followed closely by Hartford with 7,127. Bridgeport ranked third with 6,324 crashes.
Top Towns
Showing top 9 of 50 reported. 41 additional (47,065 total) not shown: Hamden, Greenwich, West Hartford, West Haven, Stratford, New Britain, East Hartford, Manchester, Bristol, Milford, Middletown, Norwich, Wallingford, Westport, North Haven, Windsor, Southington, Trumbull, Orange, Farmington, Newington, Newtown, Wethersfield, New London, Shelton, Torrington, Groton, Vernon, Bloomfield, Enfield, Berlin, Darien, Branford, Cheshire, Glastonbury, East Haven, Rocky Hill, New Milford, Plainville, Waterford, Naugatuck.
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash-level records
Road Class
Minor Arterial roads experienced the highest number of crashes, with 30,206 incidents recorded. Limited-access highways, including Interstates (15,342) and Freeways/Expressways (8,685), collectively accounted for 24,027 crashes, or 21.3% of the statewide total.
Road Class
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash-level records
Route System
Of the crashes with a recorded route system, 62,323 (58.2%) occurred on state-maintained roadways, including Interstate, US, and State routes. The remaining 44,709 crashes (41.8%) took place on local roads under municipal jurisdiction.
Route System
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash-level records
Public vs Private Road
Among crashes where roadway ownership was recorded, the overwhelming majority (106,064) occurred on public roads. A total of 4,525 crashes, representing 4.1% of this subset, took place on private property such as parking lots, shopping centers, or private driveways.
Rural vs Urban
The data indicates a strong urban concentration of traffic crashes. Of the incidents where a classification was available, 100,889 (95.6%) occurred in urban areas. Rural areas accounted for the remaining 4,590 crashes (4.4%).
Rural vs Urban
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash-level records
Junction Type
A majority of crashes, 75,514, occurred at locations not at an intersection. However, a significant number of incidents, 36,875, happened at junctions. Four-way intersections were the most common crash site among junctions with 18,413 incidents, followed by T-intersections with 16,036.
Junction Type
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash-level records
Run-off-Road / Fixed-Object Strikes
In single-vehicle, run-off-road crashes, the most commonly struck fixed objects were guardrail faces (2,723 times), walls or buildings (2,636 times), and utility poles (2,211 times). Collisions with utility poles and trees combined accounted for 3,556 incidents.
Run-off-Road / Fixed-Object Strikes
Showing top 9 of 22 reported. 13 additional (2,836 total) not shown: Traffic Sign Support, Cable Barrier, Fence, Guardrail End, Ditch, Other Traffic Barrier, Struck by Falling, Shifting Cargo or Anything Set in Motion by Motor Vehicle, Bridge Overhead Structure, Impact Attenuator/Crash Cushion, Traffic Signal Support, Bridge Rail, Culvert, Bridge Pier or Support.
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash-level records
Vehicle Type
Passenger cars were the most prevalent vehicle type involved in crashes, with 122,790 recorded. Sport Utility Vehicles were the second most common, with 48,435 involved. The data also includes notable involvement from medium/heavy trucks (5,448), motorcycles (1,160), and buses (2,017).
Vehicle Type
Showing top 9 of 19 reported. 10 additional (2,542 total) not shown: School Bus, Transit Bus, Other Bus, Moped, Motor Home, Low Speed Vehicle, All Terrain Vehicle (ATV), Motor Coach, Golf Cart, Snowmobile.
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records
Traffic Control Device
Among the 213,091 vehicles with traffic control information, 66.2% (141,011) were in locations with no control device, such as mid-block sections of road. For vehicles at controlled locations, 24.2% (51,545) were at traffic signals and 7.7% (16,368) were at stop signs.
Traffic Control Device
"Other" combines 6 smaller categories (531 records): Warning Sign (211), Marked Uncontrolled Crosswalk (191), Railway Crossing Device (48), School Zone Sign/Device (42), Pedestrian Button (38), Bicycle Detection (1).
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records
Vulnerable Road Users & Motorcycles
A total of 3,190 crashes involved either a vulnerable road user or a motorcyclist. Pedestrians were involved in 1,565 of these crashes and bicyclists in 498, together making up 64.7% of this crash subset. Motorcyclists were involved in the remaining 1,127 incidents.
Driver Contributing Action
The most frequently cited driver action contributing to a crash was 'Followed Too Closely,' which was noted for 32,927 drivers. 'Failed to Keep in Proper Lane' (20,980 drivers) and 'Failed to Yield Right-of-Way' (11,640 drivers) were the next most common contributing actions.
Driver Contributing Action
Showing top 9 of 19 reported. 10 additional (11,435 total) not shown: Ran Stop Sign, Improper Passing, Ran Red Light, Swerved or Avoided Due to Wind, Slippery Surface, Motor Vehicle, Object, Non-Motorist in Roadway, etc., Operated Motor Vehicle in Reckless or Aggressive Manner, Over-Correcting/Over-Steering, Disregarded Other Traffic Sign, Wrong Side or Wrong Way, Disregarded Other Road Markings, Overtaking Cyclist.
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events
Driver Condition
While most drivers were recorded as 'Apparently Normal', several adverse conditions were documented. A total of 2,941 drivers were noted as being under the influence of medications, drugs, or alcohol. Another 1,610 drivers were identified as being asleep or fatigued at the time of the crash.
Driver Condition
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events
Pre-Crash Driver Action
The most common action immediately preceding a crash was driving straight ahead, which was the case for 96,103 vehicles. Being stopped in traffic (23,631 vehicles) and turning left (16,125 vehicles) were the next most frequent pre-crash movements.
Pre-Crash Driver Action
Showing top 9 of 17 reported. 8 additional (13,734 total) not shown: Entering Traffic Lane, Overtaking/Passing, Other, Leaving Traffic Lane, Making U-Turn, Wrong way (or Wrong Side), Overtaking/Passing Cyclist, Traveling in Bike Lane.
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records
Point of Impact
The front of the vehicle was the most common point of initial impact, recorded in 60,195 instances (29.5%). The rear of the vehicle was the second most frequent impact point, occurring in 41,737 cases (20.5%), which aligns with the high prevalence of front-to-rear collisions.
Point of Impact
"Other" combines 9 smaller categories (32,363 records): Sector 5 (South by SouthEast) in the 12-point Clock Diagram (7,535), Sector 9 (West) in the 12-point Clock Diagram (5,894), Sector 8 (SouthWest) in the 12-point Clock Diagram (5,709), Sector 3 (East) in the 12-point Clock Diagram (5,240), Sector 4 (SouthEast) in the 12-point Clock Diagram (5,079), Non-Collision (1,435), Top (985), Undercarriage (352), Cargo loss (134).
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records
Pedestrian/Cyclist Action
Among pedestrians for whom a contributing action was noted, 'In Roadway Improperly' was the most common factor, cited in 235 cases. 'Failure to Yield Right-Of-Way' (192 cases) and 'Dart/Dash' into the roadway (169 cases) were other frequently recorded pedestrian actions.
Pedestrian/Cyclist Action
Showing top 9 of 14 reported. 5 additional (98 total) not shown: Inattentive (Talking, Eating, etc.), Improper Turn/Merge, Disabled Vehicle Related (Working on, Pushing, Leaving/Approaching), Improper Passing, Use of Electronic Device.
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Non-motorist records linked to crash events
Manner of Collision
Front-to-rear collisions were the most frequent type of crash, accounting for 41,085 incidents or 36.5% of the total. Angle collisions were the second most common type at 22,216 (19.7%), followed by same-direction sideswipes with 15,593 incidents (13.8%).
Manner of Collision
"Other" combines 1 smaller categories (915 records): Rear to rear (915).
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash-level records
Person Type
Of the 278,517 individuals involved in crashes, 72.6% were drivers (202,218) and 22.7% were passengers (63,201). The data also accounted for 1,686 pedestrians and 503 bicyclists involved in traffic incidents.
Person Type
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash-level records
Person Injury Severity
Among all 278,517 people involved in crashes, 13.4% (37,323) sustained an injury of some severity, and 0.09% (256) were fatally injured. The majority of individuals, 231,620 people, were not injured.
Person Injury Severity
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Crash-level records
Occupant Safety Equipment
Safety restraint use was high among occupants for whom data was recorded, with 190,002 individuals using both a shoulder and lap belt. However, 4,648 occupants, or approximately 2.1% of those with specified restraint use, were recorded as using no safety equipment.
Occupant Safety Equipment
"Other" combines 3 smaller categories (2,000 records): Other (1,002), Booster Seat (691), Child Restraint, Type Unknown (307).
Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Connecticut Crash Data Repository, accessed programmatically via the Socrata 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: Socrata 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 20, 2026
Data Coverage
- Reporting period: 2019-01-01 through 2019-12-31 (365 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 112,611
- Total persons involved: 278,517
- Total vehicles involved: 213,439
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 20, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Connecticut Crash Data Repository. 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 Repository ·
Period: 2019-01-01 – 2019-12-31
Generated: August 20, 2026 · All rights reserved
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