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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · DECEMBER 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/december-2019-report
Monthly Traffic Safety Analysis
10,906 CRASHES IN
CONNECTICUT, CT
DECEMBER 2019
In December 2019, Connecticut recorded 10,906 total vehicle crashes, a 9.3% increase from the 9,976 crashes documented in December 2018. Despite the rise in overall collisions, the number of fatalities fell by 34.8%, from 23 to 15. The most notable year-over-year shift was a 110% increase in crashes involving speeding, which rose from 740 to 1,555.
10,906
▲ 9.3%was 9,976
Total Crash Events
15
▼ -34.8%was 23
Persons Killed
3,328
▲ 3.2%was 3,226
Persons Injured
1,199
▲ 6.2%was 1,129
Hit-and-Run Crashes
Note: "Persons Killed" (15) counts individual fatalities across all crash events. "Fatal" in the severity table below (14) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Traffic crashes in Connecticut showed a rising trend in December 2019 compared to the same month in the prior year. Total collisions increased by 9.3% from 9,976 to 10,906. While total injuries also rose by 3.2% to 3,328, the number of fatalities decreased from 23 to 15.
1,199
Hit-and-Run Crashes — December 2019
▲ 6.2% vs prior (1,129)
The absolute number of hit-and-run incidents increased from 1,129 in December 2018 to 1,199 in December 2019, a 6.2% rise. However, due to a larger increase in overall crash volume, the hit-and-run rate as a percentage of total crashes slightly decreased. These incidents accounted for 11.0% of all crashes in the current period, compared to 11.3% in the prior year.
Vulnerable Road User Casualties
5
Pedestrians Killed
1
Cyclists Killed
9
Motorists Killed
0
Other Killed
152
Pedestrians Injured
9
Cyclists Injured
3,166
Motorists Injured
1
Other Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal pattern of crashes shifted year-over-year, with the peak day for collisions moving from Friday (1,697 crashes) in December 2018 to Monday (2,087 crashes) in December 2019. The 5 p.m. hour remained the most frequent time for crashes in both periods, though the count at this hour decreased slightly from 1,137 to 1,105.
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes increased, their overall severity trended downward year-over-year. The fatal crash rate fell from 0.21% to 0.13% of all collisions. The proportion of crashes resulting in serious injuries also decreased from 1.0% to 0.8%. In total, crashes involving any level of injury (Fatal, Serious, Minor, or Possible) constituted 22.8% of all incidents in December 2019, down from 23.7% in December 2018.
Severity is per crash event (most severe injury). 14 fatal crash events resulted in 15 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · Most severe injury per crash record
Road & Environmental Conditions
A significant shift occurred in the conditions under which crashes happened, with a substantial increase in incidents during adverse winter weather. Crashes in snowy conditions surged from 113 in December 2018 to 779 in December 2019. Correspondingly, collisions on roads with snow, ice, or slush increased from a combined 156 to 1,834. The distribution of crashes by lighting conditions remained largely consistent between the two periods.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes—Honda, Toyota, and Ford—were the same in both December 2018 and December 2019, with counts for each increasing in the latest period. Demographically, the 26-34 age group continued to be the most represented among persons involved in crashes. This group's involvement grew by 12.8% from 4,100 individuals in the prior period to 4,623 in the current period.
Top Vehicle Makes (20,233 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · Vehicle unit records
1,611 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (24,279 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph zones, the most frequent location for incidents, increased from 2,892 to 3,282 year-over-year, with the number of fatal crashes in these zones doubling from two to four. In contrast, crashes in 65 mph zones rose from 512 to 686, but associated fatalities fell from three to zero. The most significant change was the 110% increase in crashes where speeding was a factor, rising from 740 to 1,555.
Fatal crashes by zone: 25 mph: 4 of 3,282 (0.122%) · 30 mph: 1 of 944 (0.106%) · 35 mph: 2 of 1,281 (0.156%) · 40 mph: 1 of 674 (0.148%) · 45 mph: 1 of 417 (0.24%) · 50 mph: 2 of 277 (0.722%) · 55 mph: 1 of 934 (0.107%) · 88 mph: 1 of 636 (0.157%)
Source: Connecticut Crash Data · Csv Open Data · 2019-12-01 to 2019-12-31 · 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: 2019-12-01 through 2019-12-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-12-01 through 2019-12-31 (31 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 10,906
- Total persons involved: 25,906
- Total vehicles involved: 20,233
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: December 2019." Published August 20, 2026. Reporting period: 2019-12-01 to 2019-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/december-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: 2019-12-01 – 2019-12-31
Generated: August 20, 2026 · All rights reserved
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