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CRASH INTELLIGENCE REPORT · STAMFORD, CT · MAY 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/stamford/may-2019-report
Monthly Traffic Safety Analysis
440 CRASHES IN
STAMFORD, CT
MAY 2019
In May 2019, Stamford recorded 440 traffic crashes, which resulted in 0 fatalities and 133 injuries. The most common type of collision was front-to-rear, which accounted for 41.1% of all incidents. The majority of crashes, 77.3%, resulted in no injuries.
440
Total Crash Events
0
Persons Killed
133
Persons Injured
9.1%
Hit-and-Run Rate
Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Aggregate counts from crash, person, and vehicle records
40
Hit-and-Run Crashes — May 2019
Based on the initial determination of responding officers, 40 crashes in May 2019 were classified as hit-and-run incidents. This represents 9.1% of all crashes during the period. These figures reflect the events where at least one party left the scene unlawfully.
Vulnerable Road User Casualties
During this period, 126 motorists were injured in crashes, representing the largest group of injured persons. Additionally, 7 pedestrians sustained injuries. There were no fatalities recorded among motorists, pedestrians, or cyclists.
0
Pedestrians Killed
0
Motorists Killed
7
Pedestrians Injured
126
Motorists Injured
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
Crash occurrences in Stamford peaked on Fridays, which saw 80 incidents in May 2019. The most common time for crashes was the 3 p.m. hour, with 39 events. A distinct pattern emerges showing a concentration of crashes during daytime and evening commute hours, specifically from 7 a.m. through 8 p.m.
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The vast majority of crashes, 77.3% (340 incidents), resulted in no injuries, being classified as property-damage-only. The remaining 22.7% of crashes involved at least one possible, minor, or serious injury. There were no fatal crashes recorded in Stamford during this period.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Most severe injury per crash record
Road & Environmental Conditions
Most crashes occurred in favorable conditions, with 80.2% happening in daylight and 78.9% on dry road surfaces. Clear weather was reported for 73.2% of all incidents. Adverse conditions were less frequent, with 90 crashes (20.5%) on wet roads and 63 crashes (14.3%) during rain.
Weather
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Road surface condition field
Vehicles & Demographics
Among all 1,099 persons involved in crashes, the 35-44 age group was the most represented, accounting for 190 individuals. Analysis of the 878 vehicles involved shows Toyota was the most frequent make with 112 vehicles, followed by Honda with 103, and Ford with 84.
Top Vehicle Makes (878 vehicles)
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Vehicle unit records
83 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,017 persons with recorded sex)
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Person-level records linked to crash events
Speed Limit Zones
The speed limit zone designated as '1 mph' recorded the highest number of crashes, with 145 incidents, accounting for 33.0% of the total. The 25 mph zone had the next highest frequency with 90 crashes. Across all speed limit zones where crashes occurred, 0% of those crashes resulted in a fatality.
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Posted speed limit at crash location
Road Class
Crashes occurred most frequently on Minor Arterials, which accounted for 118 incidents. Combined, limited-access highways like Interstates and Freeways/Expressways were the location for 71 crashes. Principal Arterials and Local roads also saw significant crash totals, with 82 and 60 incidents respectively.
Road Class
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Crash-level records
Route System
Local roads were the site of the majority of crashes, with 224 incidents recorded. State-maintained routes, including State, US, and Interstate highways, collectively accounted for 151 crashes. This indicates that local municipal roadways saw a higher volume of crashes than those under state jurisdiction during this period.
Route System
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Crash-level records
Public vs Private Road
Among crashes where roadway ownership was documented, 393 occurred on public roads. A smaller but notable number of incidents, 33 crashes, took place on private property such as parking lots or private drives. This represents 7.7% of crashes with known ownership data.
Junction Type
The majority of crashes, 315 incidents or 71.8% of the total, occurred at locations not at an intersection. Among crashes that did happen at junctions, T-intersections were the most common site with 67 crashes, followed by four-way intersections with 49 crashes. Combined, all types of intersections accounted for 28.2% of incidents.
Junction Type
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Crash-level records
Run-off-Road / Fixed-Object Strikes
For crashes involving a collision with a fixed object, the most frequently struck objects were utility poles or light supports, which were hit 6 times. Other common objects included 'Other Fixed Object' (5 times), curbs (4 times), and fences (3 times). Combined, utility poles and trees were struck a total of 7 times.
Run-off-Road / Fixed-Object Strikes
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Crash-level records
Vehicle Type
Passenger cars were the most common vehicle type involved in crashes, accounting for 450 vehicles, followed by (Sport) Utility Vehicles with 251. Medium to heavy trucks over 10,000 lbs were involved in 21 incidents. Additionally, various types of buses were involved in 17 crashes, and motorcycles were involved in 3.
Vehicle Type
Showing top 9 of 14 reported. 5 additional (15 total) not shown: Transit Bus, Other Bus, Motorcycle, Motor Home, Moped.
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Vehicle unit records
Traffic Control Device
The majority of vehicles involved in crashes, 603 vehicles, were at locations with no traffic control device present. Traffic signals were present for 215 vehicles involved in crashes, while stop signs were the control device for 39 vehicles. This indicates that over two-thirds of involved vehicles were in uncontrolled segments of roadway.
Traffic Control Device
"Other" combines 2 smaller categories (3 records): Flashing Traffic Control Signal (2), Marked Uncontrolled Crosswalk (1).
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Vehicle unit records
Driver Contributing Action
Among drivers with a recorded contributing action, 'Followed Too Closely' was the most common, cited for 127 drivers. Other frequently noted actions included 'Failed to Keep in Proper Lane' (64 drivers), 'Improper Backing' (56 drivers), and 'Failed to Yield Right-of-Way' (52 drivers).
Driver Contributing Action
Showing top 9 of 15 reported. 6 additional (21 total) not shown: Operated Motor Vehicle in Inattentive, Careless, Negligent, or Erratic Manner, Ran Stop Sign, Operated Motor Vehicle in Reckless or Aggressive Manner, Ran Red Light, Swerved or Avoided Due to Wind, Slippery Surface, Motor Vehicle, Object, Non-Motorist in Roadway, etc., Disregarded Other Traffic Sign.
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Person-level records linked to crash events
Driver Condition
Of the drivers for whom a condition was recorded, the vast majority were listed as 'Apparently Normal'. However, 11 drivers were noted as having an abnormal condition, including 3 drivers 'Under the Influence of Medications/Drugs/Alcohol'. Other recorded conditions included being ill or fatigued, each noted for 2 drivers.
Driver Condition
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Person-level records linked to crash events
Pre-Crash Driver Action
The most common pre-crash action for vehicles was 'Straight Ahead,' which described the movement of 422 vehicles, or 48.1% of the total. A significant number of vehicles were either 'Stopped in Traffic' (88 vehicles) or 'Parked' (83 vehicles) just prior to the collision. Other notable actions included 'Backing' (69 vehicles) and 'Turning Left' (62 vehicles).
Pre-Crash Driver Action
Showing top 9 of 16 reported. 7 additional (24 total) not shown: Entering Traffic Lane, Other, Negotiating a Curve, Making U-Turn, Leaving Traffic Lane, Traveling in Bike Lane, Wrong way (or Wrong Side).
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Vehicle unit records
Point of Impact
The front of the vehicle, designated as Sector 12, was the most common point of impact, recorded for 239 vehicles. The rear of the vehicle, Sector 6, was the second most frequent impact point, involved in 204 instances. These two impact points combined represent the initial impact for over half of the vehicles in these crashes.
Point of Impact
"Other" combines 7 smaller categories (128 records): Sector 2 (NorthEast) in the 12-point Clock Diagram (37), Sector 3 (East) in the 12-point Clock Diagram (26), Sector 8 (SouthWest) in the 12-point Clock Diagram (22), Sector 4 (SouthEast) in the 12-point Clock Diagram (20), Sector 9 (West) in the 12-point Clock Diagram (20), Non-Collision (2), Undercarriage (1).
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Vehicle unit records
Manner of Collision
The most prevalent type of crash was 'Front to rear,' accounting for 181 incidents or 41.1% of all collisions. 'Sideswipe, same direction' was the second most common manner of collision with 90 crashes (20.5%), followed closely by 'Angle' collisions with 87 crashes (19.8%).
Manner of Collision
"Other" combines 1 smaller categories (6 records): Front to front (6).
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Crash-level records
Person Type
Drivers were the largest group of individuals involved in crashes, with 815 persons, making up 74.2% of the total. Passengers accounted for another 246 individuals (22.4%). A small fraction of those involved were pedestrians, with 7 individuals recorded.
Person Type
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Crash-level records
Person Injury Severity
Across all 1,099 people involved in crashes, 133 individuals sustained some level of injury, representing 12.1% of the total. Of those injured, 4 suffered serious injuries, 50 had minor injuries, and 79 had possible injuries. The majority of people, 935 individuals, were not injured.
Person Injury Severity
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Crash-level records
Occupant Safety Equipment
The majority of vehicle occupants, 699 individuals, were reported as using a shoulder and lap belt. There were 11 occupants who were recorded as not using any restraint system at the time of the crash. Additionally, 28 individuals were documented as using some form of child restraint system.
Occupant Safety Equipment
"Other" combines 2 smaller categories (3 records): Booster Seat (2), Other (1).
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Person-level records linked to crash events
Vehicles Per Crash
The vast majority of crashes, 383 incidents or 87.0%, involved two vehicles. Single-vehicle crashes were less common, accounting for 32 incidents (7.3%). There were also 25 multi-vehicle crashes involving three or more vehicles, including one incident that involved five vehicles.
Vehicles Per Crash
Source: Connecticut Crash Data Repository · Open Data · 2019-05-01 to 2019-05-31 · Crash-level records
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: 2019-05-01 through 2019-05-31
- Report generated: September 10, 2026
Data Coverage
- Reporting period: 2019-05-01 through 2019-05-31 (31 days)
- Geographic scope: Stamford, CT
- Total crash records analyzed: 440
- Total persons involved: 1,099
- Total vehicles involved: 878
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). "Stamford, CT Crash Intelligence Report: May 2019." Published September 10, 2026. Reporting period: 2019-05-01 to 2019-05-31. Data source: Connecticut Crash Data Repository. Available at: https://thatcarhitme.com/crash-data/connecticut/stamford/may-2019-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: 2019-05-01 – 2019-05-31
Generated: September 10, 2026 · All rights reserved