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Monthly Traffic Safety Analysis

9,361 CRASHES IN
CONNECTICUT, CT
JULY 2018

In July 2018, Connecticut recorded 9,361 motor vehicle crashes, resulting in 37 fatalities and 3,335 injuries. Analysis of collision types reveals that front-to-rear collisions were the most common, accounting for 37.1% of all incidents where the manner of collision was specified.

9,361

Total Crash Events

37

Persons Killed

3,335

Persons Injured

11.7%

Hit-and-Run Rate

Note: "Persons Killed" (37) counts individual fatalities across all crash events. "Fatal" in the severity table below (32) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Aggregate counts from crash, person, and vehicle records

1,099

Hit-and-Run Crashes — July 2018

In this period, 1,099 crashes were classified as hit-and-run incidents, representing 11.7% of all crashes. This designation is based on the initial determination made by the responding law enforcement officer at the scene.

Vulnerable Road User Casualties

During this period, 37 people were killed and 3,335 were injured in traffic crashes. Among these, 8 pedestrians were killed and 98 were injured. While no cyclists were killed, 48 were injured. The majority of fatalities and injuries were sustained by motorists, with 29 killed and 3,188 injured.

8

Pedestrians Killed

0

Cyclists Killed

29

Motorists Killed

0

Other Killed

98

Pedestrians Injured

48

Cyclists Injured

3,188

Motorists Injured

1

Other Injured

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Crash patterns in July 2018 show a concentration during typical commuting times. The peak day for crashes was Tuesday with 1,668 incidents, and the single busiest hour was the 4 p.m. hour with 803 crashes. The majority of collisions, 7,477 in total, occurred during daylight hours.

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The majority of crashes (74.6%) resulted in no injuries, involving only property damage. Injury-related crashes, including serious, minor, and possible injuries, accounted for a combined 25.1% of the total. There were 32 fatal crashes during this period, which resulted in a total of 37 fatalities, as a single crash can involve more than one death.

Severity is per crash event (most severe injury). 32 fatal crash events resulted in 37 persons killed.

Outcome by Severity (Crash Events)

Fatal32fatal crashes0.3%
Serious Injury131serious injury crashes1.4%
Minor Injury971minor injury crashes10.4%
Possible Injury1,242possible injury crashes13.3%
No Injury6,985no injury crashes74.6%

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Most severe injury per crash record

Road & Environmental Conditions

The vast majority of crashes occurred in favorable conditions, with 86.1% happening in clear weather and 87.2% on dry road surfaces. Similarly, 79.9% of all incidents took place during daylight hours. Crashes in adverse conditions included 869 incidents during rain and 1,118 on wet roads.

Weather

Clear8,063 (86.7%)
Rain869 (9.3%)
Cloudy356 (3.8%)
Fog, Smog, Smoke12 (0.1%)
Other4 (0.0%)
Blowing Sand, Soil, Dirt1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Weather condition at time of crash

Lighting

Daylight7,477 (80.5%)
Dark-Lighted1,211 (13.0%)
Dark-Not Lighted403 (4.3%)
Dusk102 (1.1%)
Dawn53 (0.6%)
Dark-Unknown Lighting21 (0.2%)
Other20 (0.2%)

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Lighting condition field

Road Surface

Dry8,163 (87.7%)
Wet1,118 (12.0%)
Mud, Dirt, Gravel9 (0.1%)
Standing Water4 (0.0%)
Other4 (0.0%)
Sand4 (0.0%)
Moving Water3 (0.0%)
Oil1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Road surface condition field

Vehicles & Demographics

Among all persons involved in crashes, the 26-34 age group was the most represented, with 4,044 individuals. The most frequent vehicle makes involved in these incidents were Honda (1,809 vehicles), Toyota (1,673 vehicles), and Ford (1,630 vehicles).

Top Vehicle Makes (17,854 vehicles)

1
HONDA1,809 (10.1%)
2
TOYOTA1,673 (9.4%)
3
FORD1,630 (9.1%)
4
NISSAN1,317 (7.4%)
5
CHEVROLET989 (5.5%)
6
JEEP717 (4%)
7
SUBARU657 (3.7%)
8
HYUNDAI586 (3.3%)
9
DODGE411 (2.3%)
10
BMW386 (2.2%)

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Vehicle unit records

1,619 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (22,236 persons with recorded sex)

Male12,183 (54.8%)
Female10,052 (45.2%)
01 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Person-level records linked to crash events

Speed Limit Zones

The speed zone with the highest number of crashes was 25 mph, accounting for 2,790 incidents (29.8% of the total). The percentage of crashes within a zone that resulted in a fatality tended to increase with the speed limit. For example, 0.25% of crashes in 25 mph zones were fatal, while this figure rose to 0.83% for crashes in 65 mph zones.

Fatal crashes by zone: 1 mph: 1 of 1,230 (0.081%) · 25 mph: 7 of 2,790 (0.251%) · 30 mph: 3 of 755 (0.397%) · 35 mph: 5 of 1,056 (0.473%) · 40 mph: 2 of 497 (0.402%) · 45 mph: 2 of 359 (0.557%) · 50 mph: 2 of 279 (0.717%) · 55 mph: 5 of 808 (0.619%) · 65 mph: 4 of 480 (0.833%) · 88 mph: 1 of 649 (0.154%)

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Posted speed limit at crash location

Top Counties

Crash incidents were heavily concentrated in three counties, which together accounted for 82.9% of the statewide total. Fairfield County had the highest number of crashes with 2,787 (29.8%), followed by New Haven County with 2,578 (27.5%) and Hartford County with 2,394 (25.6%).

Top Counties

1
Fairfield2,787 (29.8%)
2
New Haven2,578 (27.5%)
3
Hartford2,394 (25.6%)
4
New London591 (6.3%)
5
Middlesex337 (3.6%)
6
Litchfield334 (3.6%)
7
Tolland186 (2%)
8
Windham154 (1.6%)

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

Top Towns

The highest crash volumes at the municipal level were recorded in Connecticut's largest cities. Hartford reported the most incidents with 714 crashes, representing 7.6% of the statewide total. It was followed by New Haven with 568 crashes (6.1%) and Waterbury with 524 crashes (5.6%).

Top Towns

1
Hartford714 (9.4%)
2
New Haven568 (7.5%)
3
Waterbury524 (6.9%)
4
Bridgeport508 (6.7%)
5
Stamford470 (6.2%)
6
Norwalk349 (4.6%)
7
Danbury258 (3.4%)
8
Stratford199 (2.6%)
9
Meriden191 (2.5%)

Showing top 9 of 50 reported. 41 additional (3,794 total) not shown: Fairfield, West Hartford, Greenwich, Manchester, West Haven, New Britain, Bristol, Milford, Hamden, Middletown, East Hartford, Westport, Southington, Orange, New London, Wallingford, Norwich, North Haven, Branford, Newtown, Torrington, Trumbull, Newington, Farmington, Vernon, Windsor, Groton, Darien, Wethersfield, Bloomfield, Shelton, Waterford, Guilford, Stonington, Enfield, Glastonbury, Berlin, New Milford, Naugatuck, Plainville, Rocky Hill.

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

Road Class

Crashes were most frequent on Minor Arterial roads, which saw 2,535 incidents. Combined, limited-access highways like Interstates (1,267 crashes) and Freeways/Expressways (699 crashes) accounted for 21.0% of all crashes in the state during this period.

Road Class

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

Route System

When analyzed by route system, crashes were split between state-maintained and local roadways. State-maintained routes, including State, Interstate, and US Routes, accounted for 53.7% of all crashes (5,029 incidents). Local roads were the location for 40.3% of crashes (3,776 incidents).

Route System

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

Public vs Private Road

Among crashes where roadway ownership was documented, the vast majority occurred on public roads (8,743 incidents). A total of 440 crashes, or 4.8% of this subset, took place on private property such as parking lots or private drives.

Rural vs Urban

The data indicates that crashes predominantly occurred in urban settings, which accounted for 8,342 incidents. Crashes in areas designated as rural numbered 360, representing 4.1% of all collisions where this classification was recorded.

Rural vs Urban

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

Junction Type

The majority of crashes, 6,127 incidents, occurred at locations not at an intersection. A combined 34.4% of crashes happened at various types of junctions, with four-way intersections being the most common type (1,608 crashes), followed by T-intersections (1,432 crashes).

Junction Type

1
Not at Intersection6,127 (65.6%)
2
Four-Way Intersection1,608 (17.2%)
3
T-Intersection1,432 (15.3%)
4
Y-Intersection106 (1.1%)
5
Five-Point, or More25 (0.3%)
6
L-Intersection22 (0.2%)
7
Roundabout13 (0.1%)
8
Traffic Circle3

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

Run-off-Road / Fixed-Object Strikes

For crashes where the first harmful event was striking a fixed object, the most frequently hit objects were guardrail faces (219 incidents) and other fixed objects like walls or buildings (213 incidents). Collisions with utility poles (151) and trees (126) were also common, together representing 21.3% of all fixed-object impacts.

Run-off-Road / Fixed-Object Strikes

1
Guardrail Face219 (16.7%)
2
Other Fixed Object (wall, building, tunnel, etc.)213 (16.3%)
3
Utility Pole/Light Support151 (11.5%)
4
Tree (standing)126 (9.6%)
5
Curb116 (8.9%)
6
Other Post, Pole or Support90 (6.9%)
7
Concrete Traffic Barrier70 (5.3%)
8
Mailbox60 (4.6%)
9
Cable Barrier46 (3.5%)

Showing top 9 of 23 reported. 14 additional (218 total) not shown: Embankment, Traffic Sign Support, Fence, Guardrail End, Bridge Overhead Structure, Other Traffic Barrier, Ditch, Struck by Falling, Shifting Cargo or Anything Set in Motion by Motor Vehicle, Bridge Rail, Impact Attenuator/Crash Cushion, Traffic Signal Support, Culvert, Bridge Pier or Support, 88.

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

Vehicle Type

Passenger cars were the most common vehicle type involved in crashes, accounting for 10,372 vehicles, followed by Sport Utility Vehicles with 3,882. Medium to heavy trucks were involved in 421 incidents, representing 2.4% of all vehicles, while motorcycles accounted for 1.0% (187 vehicles).

Vehicle Type

1
Passenger Car10,372 (59.4%)
2
(Sport) Utility Vehicle3,882 (22.3%)
3
Pick Up1,230 (7%)
4
Passenger Van561 (3.2%)
5
Medium / Heavy Trucks (more than 10,000 lbs (4,536 kg))421 (2.4%)
6
Cargo Van (10,000 lbs/4,536 kg or less)234 (1.3%)
7
Other Light Trucks (10,000 lbs (4,536 kg) or less)227 (1.3%)
8
Motorcycle187 (1.1%)
9
Other150 (0.9%)

Showing top 9 of 17 reported. 8 additional (183 total) not shown: Transit Bus, Moped, School Bus, Other Bus, Motor Coach, Low Speed Vehicle, Motor Home, All Terrain Vehicle (ATV).

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Vehicle unit records

Traffic Control Device

Analysis of traffic controls present at crash locations shows that a majority of vehicles (66.0%) were involved in incidents where no control device was present. Locations with traffic signals accounted for 25.0% of vehicles in crashes, while those with stop signs accounted for 7.9%.

Traffic Control Device

"Other" combines 4 smaller categories (53 records): Person (including flagger, law enforcement, crossing guard, etc.) (31), Marked Uncontrolled Crosswalk (16), Pedestrian Button (5), Railway Crossing Device (1).

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Vehicle unit records

Vulnerable Road Users & Motorcycles

Among crashes involving motorcyclists or vulnerable road users, there were 183 motorcyclists, 113 pedestrians, and 59 bicyclists. Combined, pedestrians and bicyclists made up 48.5% of these specific incidents, totaling 172 individuals.

Driver Contributing Action

The most common contributing driver action cited was 'Followed Too Closely,' which was noted for 2,792 drivers. This was followed by 'Failed to Keep in Proper Lane' (1,671 drivers) and 'Failed to Yield Right-of-Way' (983 drivers).

Driver Contributing Action

1
No Contributing Action7,618 (48%)
2
Followed Too Closely2,792 (17.6%)
3
Failed to Keep in Proper Lane1,671 (10.5%)
4
Failed to Yield Right-of-Way983 (6.2%)
5
Improper Backing599 (3.8%)
6
Other Contributing Action454 (2.9%)
7
Improper Turn379 (2.4%)
8
Ran Off Roadway289 (1.8%)
9
Operated Motor Vehicle in Inattentive, Careless, Negligent, or Erratic Manner218 (1.4%)

Showing top 9 of 19 reported. 10 additional (872 total) not shown: Ran Stop Sign, Improper Passing, Ran Red Light, Operated Motor Vehicle in Reckless or Aggressive Manner, Swerved or Avoided Due to Wind, Slippery Surface, Motor Vehicle, Object, Non-Motorist in Roadway, etc., Wrong Side or Wrong Way, Disregarded Other Traffic Sign, Over-Correcting/Over-Steering, Disregarded Other Road Markings, Overtaking Cyclist.

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Person-level records linked to crash events

Driver Condition

Excluding drivers noted as 'Apparently Normal,' the most frequently recorded condition was driving 'Under the Influence of Medications/Drugs/Alcohol,' documented for 238 drivers. Other cited conditions included being 'Asleep or Fatigued' (136 drivers) and 'Emotional' (63 drivers).

Driver Condition

1
Apparently Normal14,857 (96.2%)
2
Under the Influence of Medications/Drugs/Alcohol238 (1.5%)
3
Asleep or Fatigued136 (0.9%)
4
Other63 (0.4%)
5
Emotional (depressed, angry, disturbed, etc.)63 (0.4%)
6
Ill (sick), Fainted48 (0.3%)
7
Physically Impaired44 (0.3%)

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Person-level records linked to crash events

Pre-Crash Driver Action

The most common pre-crash action for vehicles was proceeding 'Straight Ahead,' which was the case for 44.1% of vehicles involved (7,879). The next most frequent actions were being 'Stopped in Traffic' (1,979 vehicles) and 'Slowing' (1,306 vehicles).

Pre-Crash Driver Action

1
Straight Ahead7,879 (45.3%)
2
Stopped in Traffic1,979 (11.4%)
3
Parked1,324 (7.6%)
4
Slowing1,306 (7.5%)
5
Turning Left1,287 (7.4%)
6
Backing741 (4.3%)
7
Changing Lanes605 (3.5%)
8
Turning Right592 (3.4%)
9
Negotiating a Curve529 (3%)

Showing top 9 of 16 reported. 7 additional (1,135 total) not shown: Entering Traffic Lane, Overtaking/Passing, Other, Leaving Traffic Lane, Making U-Turn, Wrong way (or Wrong Side), Overtaking/Passing Cyclist.

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Vehicle unit records

Point of Impact

The front of the vehicle, designated as 'Sector 12 (North),' was the most common point of impact, recorded for 4,936 vehicles, or 27.6% of the total. The rear of the vehicle, 'Sector 6 (South),' was the second most frequent impact point, accounting for 3,592 vehicles (20.1%).

Point of Impact

"Other" combines 9 smaller categories (2,822 records): Sector 5 (South by SouthEast) in the 12-point Clock Diagram (612), Sector 9 (West) in the 12-point Clock Diagram (535), Sector 8 (SouthWest) in the 12-point Clock Diagram (491), Sector 4 (SouthEast) in the 12-point Clock Diagram (447), Sector 3 (East) in the 12-point Clock Diagram (420), Non-Collision (139), Top (132), Undercarriage (35), Cargo loss (11).

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Vehicle unit records

Pedestrian/Cyclist Action

Among pedestrians and cyclists involved in crashes, 69 were determined to have taken 'No Improper Action.' The most common improper actions cited were 'In Roadway Improperly' and 'Failure to Obey Traffic Signs, Signals, or Officer,' each recorded for 19 individuals. Other actions included 'Failure to Yield Right-Of-Way' (14 individuals) and 'Dart/Dash' (9 individuals).

Pedestrian/Cyclist Action

1
No Improper Action69 (42.9%)
2
In Roadway Improperly (Standing, Lying, Working, Playing)19 (11.8%)
3
Failure to Obey Traffic Signs, Signals, or Officer19 (11.8%)
4
Other14 (8.7%)
5
Failure to Yield Right-Of-Way14 (8.7%)
6
Dart/Dash9 (5.6%)
7
Not Visible (Dark Clothing, No Lighting, etc.)6 (3.7%)
8
Wrong-Way Riding or Walking4 (2.5%)
9
Inattentive (Talking, Eating, etc.)2 (1.2%)

Showing top 9 of 12 reported. 3 additional (5 total) not shown: Entering/Exiting Parked/Standing Vehicle, Disabled Vehicle Related (Working on, Pushing, Leaving/Approaching), Improper Passing.

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Non-motorist records linked to crash events

Manner of Collision

The most frequent type of collision was 'Front to rear,' which accounted for 3,476 crashes, or 37.1% of all incidents with a specified collision manner. Angle collisions were the second most common type, representing 1,826 crashes (19.5%).

Manner of Collision

"Other" combines 1 smaller categories (90 records): Rear to rear (90).

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

Person Type

Of the 23,689 individuals involved in crashes, the majority were drivers (16,773 persons, or 70.8%). Passengers constituted the next largest group with 5,677 individuals (23.9%). The data also includes 117 pedestrians and 62 bicyclists.

Person Type

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

Person Injury Severity

Across all 23,689 people involved in crashes, 37 individuals sustained fatal injuries (0.16% of all persons). A total of 3,335 people were injured, representing 14.1% of all participants. The largest group, 19,398 individuals, reported no injuries.

Person Injury Severity

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

Occupant Safety Equipment

Among motor vehicle occupants where safety equipment use was recorded, the vast majority (16,396) used a shoulder and lap belt. A total of 340 occupants were recorded as using no restraint system, accounting for 1.75% of this group.

Occupant Safety Equipment

"Other" combines 3 smaller categories (170 records): Other (79), Booster Seat (77), Child Restraint, Type Unknown (14).

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Person-level records linked to crash events

Vehicles Per Crash

The majority of incidents, 7,121 crashes or 76.1% of the total, involved two vehicles. Single-vehicle crashes accounted for 17.5% of the total, with 1,640 incidents. There was one crash reported involving six vehicles.

Vehicles Per Crash

Source: Connecticut Crash Data · Csv Open Data · 2018-07-01 to 2018-07-31 · Crash-level records

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: 2018-07-01 through 2018-07-31
  • Report generated: August 20, 2026

Data Coverage

  • Reporting period: 2018-07-01 through 2018-07-31 (31 days)
  • Geographic scope: connecticut, CT
  • Total crash records analyzed: 9,361
  • Total persons involved: 23,689
  • Total vehicles involved: 17,854

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: July 2018." Published August 20, 2026. Reporting period: 2018-07-01 to 2018-07-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/july-2018-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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