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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · JULY 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/july-2019-report
Monthly Traffic Safety Analysis
9,299 CRASHES IN
CONNECTICUT, CT
JULY 2019
In July 2019, Connecticut recorded 9,299 total traffic crashes, a slight decrease of 0.7% from the 9,361 crashes reported in July 2018. The most notable year-over-year change was a 21.6% decrease in total fatalities, which fell from 37 to 29. Total injuries also saw a minor decrease of 1.2%, from 3,335 to 3,296.
9,299
▼ -0.7%was 9,361
Total Crash Events
29
▼ -21.6%was 37
Persons Killed
3,296
▼ -1.2%was 3,335
Persons Injured
1,038
▼ -5.6%was 1,099
Hit-and-Run Crashes
Note: "Persons Killed" (29) counts individual fatalities across all crash events. "Fatal" in the severity table below (29) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, traffic crash metrics showed a slight downward trend in July 2019 compared to the same month in 2018. Total crashes decreased by 0.7% from 9,361 to 9,299. This was accompanied by a 1.2% reduction in injuries and a more significant 21.6% drop in fatalities.
1,038
Hit-and-Run Crashes — July 2019
▼ -5.6% vs prior (1,099)
Hit-and-run incidents showed a downward trend in both absolute numbers and as a percentage of total crashes. In July 2019, there were 1,038 hit-and-run crashes, accounting for 11.2% of all incidents. This is a decrease from July 2018, which recorded 1,099 hit-and-run crashes at a rate of 11.7%. The total number of hit-and-run crashes fell by 5.6% year-over-year.
Vulnerable Road User Casualties
2
Pedestrians Killed
0
Cyclists Killed
27
Motorists Killed
103
Pedestrians Injured
63
Cyclists Injured
3,130
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · 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 being the most frequent time for incidents. The peak hour for crashes in both July 2019 and July 2018 was the 4 p.m. hour, with 839 and 803 crashes respectively. However, the peak day of the week shifted from Tuesday (1,668 crashes) in 2018 to Wednesday (1,599 crashes) in 2019.
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The distribution of crash severity remained stable between July 2018 and July 2019. Fatal crashes accounted for 0.3% of all incidents in both periods, though the absolute number of fatal crashes decreased from 32 to 29. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) was nearly unchanged, moving from 25.1% in 2018 to 25.4% in 2019. Crashes with no reported injury made up the vast majority in both years, at 74.6% and 74.3% respectively.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · Most severe injury per crash record
Road & Environmental Conditions
Crashes in both periods overwhelmingly occurred in favorable conditions. In July 2019, 89.6% of crashes happened in clear weather and 90.6% on dry road surfaces. This represents a shift from July 2018, where crashes on wet roads were more frequent, accounting for 11.9% of the total compared to 8.4% in the current period. Similarly, crashes during rainfall decreased from 9.3% of the total in 2018 to 6.7% in 2019. The proportion of crashes occurring in daylight remained steady at approximately 80% for both years.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · Road surface condition field
Vehicles & Demographics
The composition of vehicles and persons involved in crashes showed minimal change year-over-year. The top five vehicle makes involved in collisions were identical in both July 2018 and July 2019: Honda, Toyota, Ford, Nissan, and Chevrolet, with counts for each make remaining very similar. The age distribution of all persons involved in crashes was also nearly identical across both periods, with the 26-34 age group representing the largest share in both years (17.1% in 2018 and 17.3% in 2019).
Top Vehicle Makes (17,851 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · Vehicle unit records
1,490 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (22,115 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across different speed limit zones remained consistent year-over-year, with zones posted at 25 mph accounting for the highest volume of crashes in both July 2018 (2,790 crashes) and July 2019 (2,847 crashes). While total fatalities decreased, the zones with the highest fatal crash rates shifted. In July 2019, the 45 mph zone had the highest rate with 5 fatal crashes out of 308 total (1.6%), whereas in July 2018, the 65 mph zone had a comparatively high rate with 4 fatal crashes out of 480 total (0.8%).
Fatal crashes by zone: 1 mph: 1 of 1,144 (0.087%) · 25 mph: 6 of 2,847 (0.211%) · 30 mph: 6 of 699 (0.858%) · 35 mph: 1 of 1,039 (0.096%) · 40 mph: 4 of 538 (0.743%) · 45 mph: 5 of 308 (1.623%) · 55 mph: 2 of 907 (0.221%) · 65 mph: 1 of 521 (0.192%) · 88 mph: 2 of 621 (0.322%)
Source: Connecticut Crash Data · Csv Open Data · 2019-07-01 to 2019-07-31 · 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: 2019-07-01 through 2019-07-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-07-01 through 2019-07-31 (31 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 9,299
- Total persons involved: 23,621
- Total vehicles involved: 17,851
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 2019." Published August 20, 2026. Reporting period: 2019-07-01 to 2019-07-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/july-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: 2019-07-01 – 2019-07-31
Generated: August 20, 2026 · All rights reserved
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