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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · AUGUST 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/august-2019-report
Monthly Traffic Safety Analysis
9,114 CRASHES IN
CONNECTICUT, CT
AUGUST 2019
In August 2019, Connecticut recorded 9,114 total crashes, a 3.5% decrease from the 9,448 crashes reported in August 2018. Despite the overall decline in collisions, the number of fatalities saw a significant year-over-year increase. Fatalities rose from 27 in the prior period to 38 in the current period, representing a 40.7% increase.
9,114
▼ -3.5%was 9,448
Total Crash Events
38
▲ 40.7%was 27
Persons Killed
3,356
▲ 1.8%was 3,297
Persons Injured
1,083
▼ -0.2%was 1,085
Hit-and-Run Crashes
Note: "Persons Killed" (38) counts individual fatalities across all crash events. "Fatal" in the severity table below (36) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash volume in Connecticut showed a downward trend, with total crashes decreasing by 3.5% from August 2018 to August 2019. However, this decline in total incidents did not correspond to a reduction in harm. The number of injuries increased slightly by 1.8%, while total fatalities rose sharply by 40.7% over the same period.
1,083
Hit-and-Run Crashes — August 2019
▼ -0.2% vs prior (1,085)
The absolute number of hit-and-run crashes remained nearly unchanged, with 1,083 incidents in August 2019 compared to 1,085 in August 2018. Because the total number of crashes decreased over the same period, the hit-and-run rate trended slightly upward. These incidents constituted 11.9% of all crashes in the current period, an increase from 11.5% in the prior year.
Vulnerable Road User Casualties
9
Pedestrians Killed
1
Cyclists Killed
28
Motorists Killed
0
Other Killed
123
Pedestrians Injured
42
Cyclists Injured
3,189
Motorists Injured
2
Other Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes remained largely consistent year-over-year, with the afternoon commute from 3 p.m. to 5 p.m. being the most frequent time for crashes in both August 2019 and August 2018. The peak hour for collisions was 5 p.m. in both periods. The peak day for crashes shifted slightly from Thursday (1,678 crashes) in 2018 to Friday (1,665 crashes) in 2019, though both days represented the highest volumes in their respective years.
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes decreased, the severity of those crashes increased from August 2018 to August 2019. The number of fatal crashes rose from 27 to 36, and the fatal crash rate increased from 0.29% to 0.39%. Crashes involving serious injuries also grew as a proportion of all incidents, from 1.1% to 1.3%. Consequently, the proportion of crashes with no reported injuries fell from 74.9% in the prior period to 73.3% in the current period.
Severity is per crash event (most severe injury). 36 fatal crash events resulted in 38 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Most severe injury per crash record
Road & Environmental Conditions
Crash conditions remained broadly similar between August 2018 and August 2019, with the vast majority of incidents in both periods occurring in daylight on dry roads. The proportion of crashes on dry roads increased from 85.6% in 2018 to 89.4% in 2019. Correspondingly, the number of crashes reported during rain fell from 908 to 695, and those on wet roads dropped from 1,274 to 892.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Road surface condition field
Vehicles & Demographics
The top five vehicle makes involved in crashes—Honda, Toyota, Ford, Nissan, and Chevrolet—were identical in both August 2019 and August 2018, with very similar incident counts. The demographic distribution of persons involved in crashes across most age groups was also stable. However, the proportion of individuals aged 65 and older involved in collisions increased from 9.3% of all persons in the prior period to 10.3% in the current period.
Top Vehicle Makes (17,406 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Vehicle unit records
1,385 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (21,535 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Person-level records linked to crash events
Speed Limit Zones
The overall distribution of crashes across different speed zones saw minimal change, with 25 mph zones accounting for the largest share of incidents in both years. However, the location of fatal crashes shifted notably year-over-year. Fatalities in 25 mph zones increased from 6 to 10, and crashes in 45 mph zones resulted in 6 deaths, up from just 1 in the prior year. Conversely, fatal crashes in 35 mph zones decreased significantly from 8 to 2.
Fatal crashes by zone: 20 mph: 1 of 50 (2%) · 25 mph: 10 of 2,865 (0.349%) · 30 mph: 5 of 638 (0.784%) · 35 mph: 2 of 957 (0.209%) · 40 mph: 3 of 517 (0.58%) · 45 mph: 6 of 350 (1.714%) · 50 mph: 1 of 240 (0.417%) · 55 mph: 3 of 896 (0.335%) · 65 mph: 4 of 526 (0.76%)
Source: Connecticut Crash Data · Csv Open Data · 2019-08-01 to 2019-08-31 · Posted speed limit at crash location
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: 2019-08-01 through 2019-08-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-08-01 through 2019-08-31 (31 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 9,114
- Total persons involved: 22,945
- Total vehicles involved: 17,406
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: August 2019." Published August 20, 2026. Reporting period: 2019-08-01 to 2019-08-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/august-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 · Csv
Period: 2019-08-01 – 2019-08-31
Generated: August 20, 2026 · All rights reserved
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