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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)

Fatal238fatal crashes0.2%
Serious Injury1,160serious injury crashes1%
Minor Injury10,731minor injury crashes9.5%
Possible Injury15,237possible injury crashes13.5%
No Injury85,245no injury crashes75.7%

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

Clear88,865 (79.4%)
Rain12,167 (10.9%)
Cloudy6,323 (5.6%)
Snow2,473 (2.2%)
Freezing Rain or Freezing Drizzle1,078 (1.0%)
Blowing Snow336 (0.3%)
Sleet or Hail297 (0.3%)
Fog, Smog, Smoke261 (0.2%)
Other104 (0.1%)
Severe Crosswinds59 (0.1%)

Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight78,949 (70.7%)
Dark-Lighted22,508 (20.1%)
Dark-Not Lighted6,921 (6.2%)
Dusk1,702 (1.5%)
Dawn843 (0.8%)
Dark-Unknown Lighting611 (0.5%)
Other171 (0.2%)

Source: Connecticut Crash Data Repository · Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry88,366 (78.9%)
Wet17,855 (15.9%)
Snow2,404 (2.1%)
Ice / Frost1,822 (1.6%)
Slush1,152 (1.0%)
Mud, Dirt, Gravel121 (0.1%)
Sand97 (0.1%)
Other76 (0.1%)
Standing Water60 (0.1%)
Moving Water38 (0.0%)

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)

1
HONDA22,512 (10.5%)
2
TOYOTA21,069 (9.9%)
3
FORD19,434 (9.1%)
4
NISSAN17,197 (8.1%)
5
CHEVROLET12,360 (5.8%)
6
SUBARU8,636 (4%)
7
JEEP8,598 (4%)
8
HYUNDAI7,531 (3.5%)
9
DODGE5,011 (2.3%)
10
BMW4,300 (2%)

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)

Male144,160 (55.1%)
Female117,439 (44.9%)
02 (0.0%)

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

1
Fairfield33,043 (29.3%)
2
New Haven31,594 (28.1%)
3
Hartford28,710 (25.5%)
4
New London6,606 (5.9%)
5
Litchfield4,015 (3.6%)
6
Middlesex3,710 (3.3%)
7
Tolland2,792 (2.5%)
8
Windham2,131 (1.9%)

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

1
New Haven7,556 (8.4%)
2
Hartford7,127 (7.9%)
3
Bridgeport6,324 (7%)
4
Waterbury5,857 (6.5%)
5
Stamford4,695 (5.2%)
6
Norwalk3,711 (4.1%)
7
Danbury3,613 (4%)
8
Fairfield2,229 (2.5%)
9
Meriden2,073 (2.3%)

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

1
Not at Intersection75,514 (67.2%)
2
Four-Way Intersection18,413 (16.4%)
3
T-Intersection16,036 (14.3%)
4
Y-Intersection1,400 (1.2%)
5
Five-Point, or More443 (0.4%)
6
L-Intersection349 (0.3%)
7
Roundabout133 (0.1%)
8
Traffic Circle101 (0.1%)

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

1
Guardrail Face2,723 (16.6%)
2
Other Fixed Object (wall, building, tunnel, etc.)2,636 (16%)
3
Utility Pole/Light Support2,211 (13.5%)
4
Other Post, Pole or Support1,367 (8.3%)
5
Tree (standing)1,345 (8.2%)
6
Curb1,246 (7.6%)
7
Concrete Traffic Barrier888 (5.4%)
8
Embankment599 (3.6%)
9
Mailbox580 (3.5%)

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

1
Passenger Car122,790 (58.8%)
2
(Sport) Utility Vehicle48,435 (23.2%)
3
Pick Up14,722 (7%)
4
Passenger Van6,613 (3.2%)
5
Medium / Heavy Trucks (more than 10,000 lbs (4,536 kg))5,448 (2.6%)
6
Other Light Trucks (10,000 lbs (4,536 kg) or less)2,826 (1.4%)
7
Cargo Van (10,000 lbs/4,536 kg or less)2,596 (1.2%)
8
Other1,746 (0.8%)
9
Motorcycle1,160 (0.6%)

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

1
No Contributing Action91,668 (47.8%)
2
Followed Too Closely32,927 (17.2%)
3
Failed to Keep in Proper Lane20,980 (10.9%)
4
Failed to Yield Right-of-Way11,640 (6.1%)
5
Improper Backing6,543 (3.4%)
6
Other Contributing Action6,120 (3.2%)
7
Improper Turn4,068 (2.1%)
8
Ran Off Roadway3,830 (2%)
9
Operated Motor Vehicle in Inattentive, Careless, Negligent, or Erratic Manner2,732 (1.4%)

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

1
Apparently Normal180,744 (96.1%)
2
Under the Influence of Medications/Drugs/Alcohol2,941 (1.6%)
3
Asleep or Fatigued1,610 (0.9%)
4
Emotional (depressed, angry, disturbed, etc.)920 (0.5%)
5
Other813 (0.4%)
6
Ill (sick), Fainted564 (0.3%)
7
Physically Impaired514 (0.3%)

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

1
Straight Ahead96,103 (46.2%)
2
Stopped in Traffic23,631 (11.4%)
3
Turning Left16,125 (7.7%)
4
Slowing15,609 (7.5%)
5
Parked14,347 (6.9%)
6
Backing7,954 (3.8%)
7
Changing Lanes7,116 (3.4%)
8
Turning Right7,040 (3.4%)
9
Negotiating a Curve6,472 (3.1%)

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

1
No Improper Action848 (43.6%)
2
In Roadway Improperly (Standing, Lying, Working, Playing)235 (12.1%)
3
Failure to Yield Right-Of-Way192 (9.9%)
4
Dart/Dash169 (8.7%)
5
Failure to Obey Traffic Signs, Signals, or Officer127 (6.5%)
6
Other117 (6%)
7
Not Visible (Dark Clothing, No Lighting, etc.)68 (3.5%)
8
Wrong-Way Riding or Walking57 (2.9%)
9
Entering/Exiting Parked/Standing Vehicle35 (1.8%)

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

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