ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MARLBOROUGH, MA · JANUARY 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/january-2023-report
Monthly Traffic Safety Analysis
83 CRASHES IN
MARLBOROUGH, MA
JANUARY 2023
Total crashes in Marlborough increased by 10.67%, from 75 in January 2022 to 83 in January 2023. A significant shift was observed in hit-and-run crashes, which rose by 700%, from 1 to 8 incidents. Conversely, DUI-related crashes decreased by 66.67% during the same period.
83
▲ 10.7%was 75
Total Crash Events
0
Persons Killed
20
▲ 5.3%was 19
Persons Injured
8
▲ 700.0%was 1
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 · 2023-01-01 to 2023-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash incidents in Marlborough saw an upward trend, increasing by 10.67% year-over-year, from 75 crashes in January 2022 to 83 crashes in January 2023. Total injuries also experienced a slight increase of 5.26%, rising from 19 to 20. Fatalities remained at zero in both periods.
8
Hit-and-Run Crashes — January 2023
▲ 700.0% vs prior (1)
Hit-and-run crashes increased substantially year-over-year, rising by 700% from 1 incident in January 2022 to 8 incidents in January 2023. This resulted in the hit-and-run crash rate increasing from 1.3% to 9.6% of all crashes. The trend for hit-and-run incidents is significantly upward.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
1
Pedestrians Injured
1
Cyclists Injured
18
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak day for crashes remained Monday in both periods, with an increase from 18 crashes in January 2022 to 22 crashes in January 2023. Similarly, the peak hour for crashes was 4 PM in both years, rising from 10 incidents in the prior period to 13 in the current period. Notably, crashes on Friday increased by 85.71%, from 7 to 13, while Saturday crashes decreased by 18.75%, from 16 to 13.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatalities remained at zero in both January 2022 and January 2023. While overall injuries increased slightly from 19 to 20, the distribution of injury severity shifted. Serious injuries (Severity A) decreased by 33.33% from 3 to 2, and possible injuries (Severity C) also decreased by 33.33% from 3 to 2. Minor injuries (Severity B) saw a substantial increase of 160%, rising from 5 to 13.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Most severe injury per crash record
Top Contributing Factors
The count of "No improper driving" as a contributing factor increased by 107.69%, rising from 13 in January 2022 to 27 in January 2023. "Inattention" also increased by 50% in count, from 8 to 12. Conversely, "Failure to keep in proper lane or running off road" decreased by 42.86% in count, from 7 to 4, and "Exceeded authorized speed limit" decreased by 33.33% in count, from 3 to 2.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in "Clear" weather conditions increased by 11.9%, from 42 in January 2022 to 47 in January 2023. Crashes on "Dry" road surfaces increased by 15.56%, from 45 to 52, and those on "Wet" surfaces increased by 45.45%, from 11 to 16. Crashes during "Daylight" conditions increased by 19.51%, from 41 to 49, while those in "Dark - lighted roadway" conditions decreased by 12%, from 25 to 22.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Road surface condition field
Vehicles & Demographics
The age distribution of persons involved in crashes showed some shifts year-over-year. The 26-34 age group saw a notable 73.91% increase in persons involved, rising from 23 to 40. Conversely, the 21-25 age group experienced a 33.33% decrease in involvement, from 24 to 16. Among vehicle makes, TOYOTA and HONDA continued to be the most frequently involved, with TOYOTA increasing by 20% (from 25 to 30) and HONDA by 41.18% (from 17 to 24).
Top Vehicle Makes (156 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Vehicle unit records
19 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (156 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 MPH zones increased by 18.75%, from 16 in January 2022 to 19 in January 2023. Crashes in 30 MPH zones also increased by 17.65%, from 17 to 20. Conversely, crashes in 35 MPH zones decreased by 28.57%, from 14 to 10. There were no fatal crashes reported in any speed limit zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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: 2023-01-01 through 2023-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-01-31 (31 days)
- Geographic scope: MARLBOROUGH, MA
- Total crash records analyzed: 83
- Total persons involved: 176
- Total vehicles involved: 156
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: January 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/marlborough/january-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: 2023-01-01 – 2023-01-31
Generated: June 21, 2026 · All rights reserved