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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · JANUARY 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/january-2019-report
Monthly Traffic Safety Analysis
9,166 CRASHES IN
CONNECTICUT, CT
JANUARY 2019
In January 2019, there were 9,166 total crashes recorded in Connecticut, representing a 10.3% decrease from the 10,215 crashes in January 2018. Despite this overall reduction in collisions, the number of fatal crashes increased from 17 to 19 year-over-year. Total injuries and fatalities saw slight decreases of 4.9% and 4.8%, respectively.
9,166
▼ -10.3%was 10,215
Total Crash Events
20
▼ -4.8%was 21
Persons Killed
2,730
▼ -4.9%was 2,871
Persons Injured
1,045
▼ -7.0%was 1,124
Hit-and-Run Crashes
Note: "Persons Killed" (20) counts individual fatalities across all crash events. "Fatal" in the severity table below (19) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Comparing January 2019 to the same month in 2018, key traffic safety metrics show a general downward trend. Total crashes fell by 10.3% from 10,215 to 9,166. Similarly, the number of people injured decreased by 4.9% from 2,871 to 2,730, and total fatalities dropped slightly from 21 to 20.
1,045
Hit-and-Run Crashes — January 2019
▼ -7.0% vs prior (1,124)
The total number of hit-and-run crashes decreased from 1,124 in January 2018 to 1,045 in January 2019. However, as a proportion of all collisions, the hit-and-run rate trended slightly upward. Hit-and-run crashes accounted for 11.4% of all crashes in the current period, compared to 11.0% in the prior year.
Vulnerable Road User Casualties
7
Pedestrians Killed
0
Cyclists Killed
13
Motorists Killed
0
Other Killed
101
Pedestrians Injured
15
Cyclists Injured
2,613
Motorists Injured
1
Other Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · 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 between January 2018 and January 2019. Tuesday was the peak day for crashes in both periods, with 1,601 incidents in the current period compared to 1,956 in the prior year. The 5 p.m. hour was also the consistent peak hour for collisions, recording 885 crashes in January 2019, a slight increase from 876 crashes in January 2018.
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes decreased year-over-year, the rate of fatal crashes per 100 collisions increased from 0.17 to 0.21. The number of fatal crashes rose from 17 in January 2018 to 19 in January 2019. The proportion of crashes resulting in any injury (fatal, serious, minor, or possible) increased slightly from 20.8% to 22.1% of all crashes.
Severity is per crash event (most severe injury). 19 fatal crash events resulted in 20 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · Most severe injury per crash record
Road & Environmental Conditions
The distribution of crashes across different environmental conditions showed some shifts between January 2018 and January 2019. The proportion of crashes occurring on dry road surfaces increased from 63.8% to 70.5% year-over-year, while the share of crashes on snow-covered roads fell from 9.5% to 4.4%. Crashes during daylight hours accounted for 58.6% of the total in January 2019, down slightly from 60.7% in the prior year. The percentage of collisions in clear weather remained stable at 78.5% for both periods.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in collisions shifted slightly year-over-year. In January 2019, Toyota (1,747 vehicles) and Honda (1,733 vehicles) were the most common makes, overtaking Ford, which was the top make in January 2018 with 1,883 vehicles. The age distribution of all persons involved in crashes remained stable between the two periods, with the 26-34 age group consistently being the largest, accounting for 17.4% of individuals in the current period and 17.5% in the prior period.
Top Vehicle Makes (17,072 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · Vehicle unit records
1,478 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (20,396 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph zones were the most common in both periods, though the count decreased from 3,176 to 2,818 year-over-year, while the number of fatal crashes in this zone held steady at 6. There was a notable increase in fatal crashes in some higher speed zones. In 55 mph zones, fatal crashes rose from zero in January 2018 to 3 in January 2019, and fatalities in 35 mph zones increased from 1 to 3.
Fatal crashes by zone: 25 mph: 6 of 2,818 (0.213%) · 35 mph: 3 of 1,097 (0.273%) · 40 mph: 2 of 554 (0.361%) · 45 mph: 3 of 372 (0.806%) · 55 mph: 3 of 730 (0.411%) · 65 mph: 2 of 527 (0.38%)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-01-31 · 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: 2019-01-01 through 2019-01-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-01-01 through 2019-01-31 (31 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 9,166
- Total persons involved: 21,754
- Total vehicles involved: 17,072
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: January 2019." Published August 20, 2026. Reporting period: 2019-01-01 to 2019-01-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/january-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: 2019-01-01 – 2019-01-31
Generated: August 20, 2026 · All rights reserved
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