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ThatCarHitMe.com
An Injuria.ai Company
CRASH INTELLIGENCE REPORT · CONNECTICUT, CT · 2018
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/2018-annual-report
Yearly Traffic Safety Analysis
2,120 CRASHES IN
CONNECTICUT, CT
2018
In 2018, Windham County recorded 2,120 traffic crashes, resulting in 13 fatalities and 733 injuries. These incidents involved 4,715 people and 3,533 vehicles. A notable temporal pattern emerged from the data, with crashes peaking on Fridays (365 incidents) and during the 3 p.m. hour (176 incidents), indicating a strong correlation with weekday afternoon commute times.
2,120
Total Crash Events
13
Persons Killed
733
Persons Injured
8.3%
Hit-and-Run Rate
Note: "Persons Killed" (13) counts individual fatalities across all crash events. "Fatal" in the severity table below (13) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records
176
Hit-and-Run Crashes — 2018
A total of 176 crashes, representing 8.3% of all incidents in Windham County, were classified as hit-and-run events. This classification is based on the initial determination made by the responding law enforcement officer at the scene of the crash. These incidents contributed to the overall crash statistics for the year.
Vulnerable Road User Casualties
Of the 13 total fatalities recorded, all were vehicle motorists. An additional 718 motorists sustained injuries. No pedestrians or cyclists were killed in 2018. However, 13 pedestrians and 2 cyclists were injured in traffic crashes during this period.
0
Pedestrians Killed
0
Cyclists Killed
13
Motorists Killed
13
Pedestrians Injured
2
Cyclists Injured
718
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
Crash frequency in Windham County shows distinct daily and weekly patterns. The data indicates that Friday was the most common day for crashes, with 365 incidents, while the afternoon commute hour of 3 p.m. was the single busiest hour with 176 crashes. Overall, a majority of crashes, 1,418 or 67%, occurred during daylight hours.
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The majority of crashes, 73.4% (1,557 incidents), resulted in no injuries. Injury-involved crashes, including serious, minor, and possible injuries, accounted for 26% of the total (550 incidents). There were 13 fatal crashes, representing 0.6% of all crashes, which resulted in a total of 13 fatalities.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record
Road & Environmental Conditions
A substantial majority of crashes in Windham County occurred under ideal driving conditions. Specifically, 70.5% of crashes (1,495) happened on dry road surfaces, 75.3% (1,596) in clear weather, and 66.9% (1,418) during daylight hours. Crashes in adverse conditions were less frequent, with 236 incidents occurring during rain and 367 on wet roads.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field
Vehicles & Demographics
Among the 4,715 individuals involved in crashes, the 26-34 age group was the most represented with 775 people, followed by the 35-44 and 45-54 age groups. An analysis of the 3,533 vehicles involved shows that Ford was the most frequent make, appearing in 504 crash records. Other common makes included Toyota (244 vehicles) and Chevrolet (231 vehicles).
Top Vehicle Makes (3,533 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records
136 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (4,537 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Person-level records linked to crash events
Speed Limit Zones
The 25 mph speed zone saw the highest number of crashes, with 560 incidents, accounting for 26.4% of the total. While lower speed zones had more crashes, the percentage of crashes that were fatal increased with speed. For instance, in the 45 mph zone, which had 259 crashes, 2.32% of those crashes were fatal, a significantly higher rate than the 0.18% fatal crash rate in the 25 mph zone.
Fatal crashes by zone: 25 mph: 1 of 560 (0.179%) · 30 mph: 1 of 238 (0.42%) · 35 mph: 1 of 343 (0.292%) · 40 mph: 1 of 204 (0.49%) · 45 mph: 6 of 259 (2.317%) · 50 mph: 1 of 62 (1.613%) · 65 mph: 2 of 234 (0.855%)
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Posted speed limit at crash location
Top Towns
The 2,120 crashes in the county were geographically concentrated in a few key towns. The town of Windham experienced the highest number of incidents with 510 crashes, representing 24.1% of the county's total. Following Windham were Killingly with 430 crashes (20.3%) and Plainfield with 293 crashes (13.8%).
Top Towns
Showing top 9 of 15 reported. 6 additional (216 total) not shown: Ashford, Chaplin, Sterling, Hampton, Scotland, Eastford.
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash-level records
Road Class
Collector roads were the site of the most crashes in Windham County, accounting for 511 incidents. Principal Arterials (437 crashes) and Minor Arterials (412 crashes) also saw significant crash volumes. Limited-access highways, including Interstates and Freeways/Expressways, collectively accounted for 309 crashes, or 14.6% of the total.
Road Class
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash-level records
Route System
An analysis of roadway jurisdiction shows that state-maintained routes (including State, US, and Interstate routes) were the location for 1,391 crashes. This represents 67.2% of crashes on roads with a known system type. Locally maintained roads accounted for the remaining 679 crashes, or 32.8% of the total.
Route System
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash-level records
Public vs Private Road
The vast majority of crashes occurred on public roadways, with 2,060 incidents recorded. A small fraction, 41 crashes or approximately 2%, took place on private roads, which can include locations like parking lots or private driveways.
Rural vs Urban
Crashes in Windham County were more common in areas designated as urban, which accounted for 1,385 incidents. Rural areas saw 658 crashes, representing 32.2% of the total where the designation was known. This highlights that nearly one-third of all crashes occurred in rural settings.
Rural vs Urban
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash-level records
Junction Type
The majority of crashes, 1,460 incidents or 68.9%, occurred at locations not at an intersection. For crashes that did happen at junctions, T-intersections were the most common type with 321 crashes, followed by four-way intersections with 291 crashes. Combined, all types of intersections accounted for 31% of total crashes.
Junction Type
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash-level records
Run-off-Road / Fixed-Object Strikes
Among single-vehicle crashes involving a fixed object, the most commonly struck object was a guardrail face, which was hit 150 times. This was followed by utility poles (109 times) and trees (103 times). Combined, collisions with trees and utility poles accounted for 212 incidents, representing 29.3% of all recorded fixed-object crashes.
Run-off-Road / Fixed-Object Strikes
Showing top 9 of 21 reported. 12 additional (94 total) not shown: Guardrail End, Curb, Traffic Sign Support, Fence, Other Traffic Barrier, Concrete Traffic Barrier, Culvert, Bridge Pier or Support, Impact Attenuator/Crash Cushion, Struck by Falling, Shifting Cargo or Anything Set in Motion by Motor Vehicle, Bridge Rail, Traffic Signal Support.
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash-level records
Vehicle Type
Passenger cars were the most prevalent vehicle type involved in crashes, with 2,086 units recorded. Utility vehicles (612) and pickup trucks (433) were the next most common. Notably, crashes also involved 71 medium or heavy trucks, 43 motorcycles, and 12 school buses.
Vehicle Type
Showing top 9 of 16 reported. 7 additional (30 total) not shown: School Bus, Moped, Motor Home, Other Bus, Low Speed Vehicle, All Terrain Vehicle (ATV), Transit Bus.
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records
Traffic Control Device
Analysis of traffic controls at crash locations, based on 3,528 vehicle records, shows the majority occurred where no control device was present (2,523 vehicles). A significant number also occurred at locations with traffic control signals (624 vehicles) or stop signs (316 vehicles). This indicates that most incidents happened on uncontrolled road segments rather than at controlled intersections.
Traffic Control Device
"Other" combines 4 smaller categories (8 records): Other (4), Marked Uncontrolled Crosswalk (2), Yield Sign (1), Bicycle Detection (1).
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records
Vulnerable Road Users & Motorcycles
In 2018, there were 60 crashes involving vulnerable road users or motorcyclists. Motorcyclists were the largest group with 41 incidents. The remaining crashes involved pedestrians (16) and bicyclists (3), with these two groups of vulnerable road users accounting for 19 incidents, or 31.7% of the total in this category.
Driver Contributing Action
Among drivers for whom a contributing action was cited, the most common error was failing to keep in the proper lane, attributed to 470 drivers. The second most frequent action was following too closely, noted for 441 drivers. Failing to yield the right-of-way was the third most common contributing action, recorded for 262 drivers.
Driver Contributing Action
Showing top 9 of 18 reported. 9 additional (264 total) not shown: Ran Stop Sign, Swerved or Avoided Due to Wind, Slippery Surface, Motor Vehicle, Object, Non-Motorist in Roadway, etc., Improper Passing, Over-Correcting/Over-Steering, Ran Red Light, Operated Motor Vehicle in Reckless or Aggressive Manner, Disregarded Other Traffic Sign, Wrong Side or Wrong Way, Disregarded Other Road Markings.
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Person-level records linked to crash events
Driver Condition
While most drivers were recorded as 'Apparently Normal', 246 drivers were noted as having a condition that may have contributed to the crash. The most common of these was being under the influence of medications, drugs, or alcohol, which was recorded for 112 drivers. An additional 66 drivers were identified as being asleep or fatigued at the time of their crash.
Driver Condition
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Person-level records linked to crash events
Pre-Crash Driver Action
The most common pre-crash action for vehicles was driving straight ahead, which was the case for 1,654 vehicles involved in collisions. Negotiating a curve and turning left were the next most frequent actions, each recorded for 309 vehicles. These actions represent the moments immediately preceding the crash event.
Pre-Crash Driver Action
Showing top 9 of 17 reported. 8 additional (230 total) not shown: Overtaking/Passing, Changing Lanes, Other, Making U-Turn, Leaving Traffic Lane, Wrong way (or Wrong Side), Traveling in Bike Lane, Overtaking/Passing Cyclist.
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records
Point of Impact
The front of the vehicle was the most common point of impact, identified as 'Sector 12 (North)' in 988 instances, or 28.2% of impacts where the location was specified. The rear of the vehicle, 'Sector 6 (South)', was the second most frequent impact point, recorded 504 times (14.4%). This suggests a high prevalence of frontal and rear-end collisions.
Point of Impact
"Other" combines 9 smaller categories (518 records): Sector 9 (West) in the 12-point Clock Diagram (111), Sector 5 (South by SouthEast) in the 12-point Clock Diagram (97), Sector 8 (SouthWest) in the 12-point Clock Diagram (96), Sector 3 (East) in the 12-point Clock Diagram (93), Sector 4 (SouthEast) in the 12-point Clock Diagram (65), Non-Collision (27), Undercarriage (14), Top (13), Cargo loss (2).
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records
Pedestrian/Cyclist Action
Of the 16 pedestrians involved in crashes, 6 were determined to have taken no improper action. For those with contributing actions, behaviors included wrong-way walking (2 pedestrians), being in the roadway improperly (2), and not being visible in dark clothing (2).
Pedestrian/Cyclist Action
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Non-motorist records linked to crash events
Manner of Collision
The most frequent type of collision was front-to-rear, which accounted for 536 crashes or 25.3% of the total. Angle collisions were the second most common pattern, with 406 incidents representing 19.2% of all crashes. These two types alone constitute nearly half of all multi-vehicle crashes.
Manner of Collision
"Other" combines 1 smaller categories (6 records): Rear to rear (6).
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash-level records
Person Type
Of the 4,715 people involved in crashes, the vast majority were drivers, accounting for 3,393 individuals or 71.9% of the total. Passengers made up the next largest group with 1,080 people (22.9%). A small number of pedestrians (16) and bicyclists (3) were also involved.
Person Type
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash-level records
Person Injury Severity
Among all 4,715 individuals involved in crashes, 79.5% (3,747 people) were not injured. A total of 733 people, or 15.5%, sustained some level of injury, ranging from possible to serious. Fatal injuries were recorded for 13 individuals, representing 0.28% of all persons involved.
Person Injury Severity
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Crash-level records
Occupant Safety Equipment
Based on records for 4,085 vehicle occupants where restraint use was noted, the vast majority utilized safety equipment. However, 71 individuals, or 1.7% of this group, were recorded as not using any restraint system. The most common restraint type used was a shoulder and lap belt, reported for 3,637 occupants.
Occupant Safety Equipment
"Other" combines 3 smaller categories (29 records): Booster Seat (15), Other (8), Child Restraint, Type Unknown (6).
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · Person-level records linked to crash events
Vehicles Per Crash
Two-vehicle collisions were the most common crash configuration, accounting for 1,205 incidents or 56.8% of the total. Single-vehicle crashes were also frequent, with 818 incidents making up 38.6% of all crashes. Multi-vehicle pile-ups involving three or more vehicles were less common, with 97 such incidents recorded.
Vehicles Per Crash
Source: Connecticut Crash Data · Csv Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2018-01-01 through 2018-12-31 (365 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 2,120
- Total persons involved: 4,715
- Total vehicles involved: 3,533
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: 2018." Published August 20, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2018-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: 2018-01-01 – 2018-12-31
Generated: August 20, 2026 · All rights reserved
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