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ThatCarHitMe.com
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YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · JULY 2023
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-2023-report
Monthly Traffic Safety Analysis
8,555 CRASHES IN
CONNECTICUT, CT
JULY 2023
In July 2023, Connecticut recorded 8,555 total vehicle crashes, a 4.0% increase from the 8,224 crashes reported in July 2022. While total injuries remained stable, fatalities rose from 35 to 40. The most significant year-over-year change was a 56.1% increase in crashes where speeding was a factor, which grew from 367 to 573 incidents.
8,555
▲ 4.0%was 8,224
Total Crash Events
40
▲ 14.3%was 35
Persons Killed
3,043
▼ -0.4%was 3,056
Persons Injured
1,055
▼ -4.9%was 1,109
Hit-and-Run Crashes
Note: "Persons Killed" (40) counts individual fatalities across all crash events. "Fatal" in the severity table below (39) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic crash volume in Connecticut increased by 4.0% in July 2023 compared to the same month in the prior year, rising from 8,224 to 8,555 incidents. This increase was accompanied by a 14.3% rise in total fatalities, from 35 to 40. However, the total number of injuries reported saw a marginal decrease of 0.4%, from 3,056 to 3,043.
1,055
Hit-and-Run Crashes — July 2023
▼ -4.9% vs prior (1,109)
The number of hit-and-run incidents decreased from July 2022 to July 2023. There were 1,055 hit-and-run crashes in the current period, a 4.9% reduction from the 1,109 recorded in the prior year. The hit-and-run rate, which measures the proportion of all crashes that are hit-and-runs, also trended downward, falling from 13.5% to 12.3%.
Vulnerable Road User Casualties
4
Pedestrians Killed
1
Cyclists Killed
35
Motorists Killed
59
Pedestrians Injured
50
Cyclists Injured
2,934
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes showed notable shifts between July 2022 and July 2023. The peak day for crashes moved from Friday (1,586 crashes) in the prior year to Saturday (1,403 crashes) in the current period. Similarly, the peak hour for collisions shifted one hour earlier, from 4 p.m. in 2022 to 3 p.m. in 2023. A significant redistribution of weekly crashes occurred, with Monday crashes increasing by 40.9% while Friday crashes decreased by 22.2%.
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes shifted slightly year-over-year, with a higher number of fatal incidents. The count of fatal crashes increased from 34 to 39, raising the fatal crash rate from 0.41 to 0.46 per 100 crashes. Conversely, crashes resulting in serious injuries decreased from 132 to 122, a drop from 1.6% to 1.4% of all incidents. The proportion of crashes with no injuries increased slightly from 73.3% to 73.8%.
Severity is per crash event (most severe injury). 39 fatal crash events resulted in 40 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · Most severe injury per crash record
Road & Environmental Conditions
Crashes occurring in adverse weather and road conditions increased significantly year-over-year. The number of crashes in the rain more than doubled, rising from 326 in July 2022 to 809 in July 2023. Similarly, collisions on wet road surfaces increased by 157%, from 445 to 1,145. As a proportion of total crashes, incidents on wet roads grew from 5.4% to 13.4%. In contrast, the distribution of crashes by lighting conditions remained relatively stable, with about 78% of incidents in both periods occurring in daylight.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · Road surface condition field
Vehicles & Demographics
The composition of vehicles involved in crashes remained consistent year-over-year. The top five most frequently involved vehicle makes—Honda, Toyota, Ford, Nissan, and Chevrolet—were identical in both July 2022 and July 2023, with only minor changes in their respective counts. The age distribution of persons involved in crashes also showed little change, with all age groups maintaining a stable proportional representation between the two periods.
Top Vehicle Makes (16,123 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · Vehicle unit records
1,360 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (19,065 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · Person-level records linked to crash events
Speed Limit Zones
There was a noticeable shift in crashes toward higher speed zones in July 2023 compared to the prior year. The number of crashes in 65 mph zones increased by 24.0%, from 487 to 604. Fatal crashes in these high-speed zones also doubled, rising from 3 to 6. Additionally, fatal crashes increased in 30 mph zones (from 3 to 7) and 40 mph zones (from 4 to 7), while decreasing in 55 mph zones (from 7 to 3).
Fatal crashes by zone: 25 mph: 7 of 2,322 (0.301%) · 30 mph: 7 of 631 (1.109%) · 35 mph: 7 of 909 (0.77%) · 40 mph: 7 of 474 (1.477%) · 50 mph: 1 of 241 (0.415%) · 55 mph: 3 of 913 (0.329%) · 65 mph: 6 of 604 (0.993%)
Source: Connecticut Crash Data · Csv Open Data · 2023-07-01 to 2023-07-31 · 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: 2023-07-01 through 2023-07-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2023-07-01 through 2023-07-31 (31 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 8,555
- Total persons involved: 20,560
- Total vehicles involved: 16,123
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 2023." Published August 20, 2026. Reporting period: 2023-07-01 to 2023-07-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/july-2023-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: 2023-07-01 – 2023-07-31
Generated: August 20, 2026 · All rights reserved
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