ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MARLBOROUGH, MA · NOVEMBER 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/massachusetts/marlborough/november-2023-report
Monthly Traffic Safety Analysis
75 CRASHES IN
MARLBOROUGH, MA
NOVEMBER 2023
Total crashes in MARLBOROUGH, MA decreased by 22.7% from 97 in November 2022 to 75 in November 2023. Concurrently, total injuries decreased by 46.9%, from 32 to 17. The number of crashes involving a pedestrian increased from 1 to 4, representing a 300% rise year-over-year.
75
▼ -22.7%was 97
Total Crash Events
0
Persons Killed
17
▼ -46.9%was 32
Persons Injured
8
▲ 33.3%was 6
Hit-and-Run Crashes
Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 4 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a decrease in crash activity year-over-year, with total crashes falling by 22.7% from 97 to 75. This reduction is also reflected in a substantial 46.9% decrease in total injuries, from 32 to 17. Fatalities remained at zero in both periods.
8
Hit-and-Run Crashes — November 2023
▲ 33.3% vs prior (6)
Hit-and-run crashes increased from 6 in November 2022 to 8 in November 2023. The hit-and-run rate also increased, rising from 6.2% to 10.7% of all crashes year-over-year.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
3
Pedestrians Injured
14
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
Wednesday remained the peak day for crashes in both periods, though the count decreased from 19 to 16. The peak hour for crashes also remained consistent at 5 PM, with 13 crashes in both November 2022 and November 2023. Notably, crashes on Thursdays doubled from 7 to 14, while crashes on Sundays decreased from 15 to 9.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatal crashes in either period. Serious injuries, which accounted for 5 crashes (5.2%) in November 2022, were not reported in November 2023. Minor injury crashes decreased from 15 (15.5%) to 6 (8%), while possible injury crashes increased from 3 (3.1%) to 8 (10.7%).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Most severe injury per crash record
Top Contributing Factors
"No improper driving" remained the most frequent contributing factor, decreasing slightly from 19 crashes to 17 crashes. "Failed to yield right of way" saw a significant decrease, dropping from 17 crashes in the prior period to 8 crashes in the current period. "Followed too closely" and "Inattention" both decreased by one crash, from 14 to 13 each.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in 'Clear' weather conditions decreased from 84 to 62. Crashes on 'Wet' road surfaces doubled from 5 to 10, while crashes on 'Dry' road surfaces decreased from 90 to 64. Crashes occurring during 'Daylight' hours decreased from 49 to 38, and those in 'Dark - lighted roadway' conditions decreased from 28 to 26.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Road surface condition field
Vehicles & Demographics
The total number of persons involved in crashes decreased from 206 to 174. The age group 35-44 saw an increase in persons involved from 32 to 36, while the 16-20 age group increased slightly from 20 to 21. Conversely, female persons involved decreased significantly from 91 to 60, while male persons involved increased from 95 to 98. Toyota remained the top vehicle make involved in crashes, though its count decreased from 40 to 31, while Honda vehicles increased from 15 to 19.
Top Vehicle Makes (141 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Vehicle unit records
16 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (158 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Person-level records linked to crash events
Speed Limit Zones
No fatal crashes were recorded in any speed zone during either period. Crashes in 30 mph zones decreased from 32 to 24, and those in 35 mph zones decreased from 18 to 14. Crashes in 10 mph zones increased from 2 to 3, while 5 mph and 20 mph zones, which had 1 and 2 crashes respectively in the prior period, reported no crashes in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Posted speed limit at crash location
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Massachusetts Crash Data (MassDOT CDV), accessed programmatically via the Arcgis_yearly 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: Arcgis_yearly 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: 2023-11-01 through 2023-11-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-11-01 through 2023-11-30 (30 days)
- Geographic scope: MARLBOROUGH, MA
- Total crash records analyzed: 75
- Total persons involved: 174
- Total vehicles involved: 141
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). "MARLBOROUGH, MA Crash Intelligence Report: November 2023." Published June 21, 2026. Reporting period: 2023-11-01 to 2023-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/marlborough/november-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: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2023-11-01 – 2023-11-30
Generated: June 21, 2026 · All rights reserved