ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MARLBOROUGH, MA · OCTOBER 2022
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/october-2022-report
Monthly Traffic Safety Analysis
88 CRASHES IN
MARLBOROUGH, MA
OCTOBER 2022
Total crashes in Marlborough decreased by 12.87% year-over-year, from 101 crashes in October 2021 to 88 crashes in October 2022. Despite this reduction in overall incidents, total injuries rose by 25%, from 20 to 25. This indicates a notable shift towards higher injury severity in the fewer crashes that occurred.
88
▼ -12.9%was 101
Total Crash Events
0
Persons Killed
25
▲ 25.0%was 20
Persons Injured
12
▲ 20.0%was 10
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. 6 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a decrease in total crashes, with a 12.87% reduction from 101 crashes in October 2021 to 88 crashes in October 2022. However, total injuries increased by 25% during the same period, rising from 20 to 25. Fatalities remained at zero in both periods.
12
Hit-and-Run Crashes — October 2022
▲ 20.0% vs prior (10)
Hit-and-run crashes increased from 10 in October 2021 to 12 in October 2022, representing a 20% rise in incidents. The hit-and-run rate also increased from 9.9% to 13.6% of all crashes, indicating an upward trend in the proportion of crashes involving a hit-and-run incident.
Vulnerable Road User Casualties
0
Cyclists Killed
0
Motorists Killed
1
Cyclists Injured
24
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak day for crashes shifted from Friday in October 2021 to Saturday in October 2022, with both days recording 22 crashes. The peak hour for crashes also shifted from 11 AM with 13 crashes in October 2021 to 2 PM with 10 crashes in October 2022. This suggests a shift in crash concentration towards weekend afternoons.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes remained at zero in both October 2021 and October 2022. While total injuries increased from 20 to 25, the distribution of injury severity shifted; serious injuries (code A) decreased from 2 to 0, minor injuries (code B) decreased from 7 to 5, and possible injuries (code C) increased from 4 to 12. The proportion of crashes resulting in 'No Injury' decreased from 83.2% to 73.9%.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Most severe injury per crash record
Top Contributing Factors
Crashes attributed to 'No improper driving' decreased by 9 incidents, from 37 in October 2021 to 28 in October 2022, a 24.3% reduction. Conversely, 'Inattention' increased by 6 crashes, from 13 to 19, a 46.2% rise, and 'Failed to yield right of way' increased by 3 crashes, from 9 to 12. 'Followed too closely' decreased by 5 crashes, from 14 to 9, a 35.7% reduction.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in 'Clear' weather increased from 62 to 68, while those in 'Rain' decreased significantly from 20 to 6. Similarly, crashes on 'Wet' road surfaces decreased from 30 to 15, while those on 'Dry' surfaces increased from 69 to 73. The proportion of crashes occurring in 'Daylight' conditions decreased from 71 to 61, while crashes in 'Dark - lighted roadway' increased from 18 to 21.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased from 190 in October 2021 to 178 in October 2022. The 26-34 age group remained the highest represented, though its count decreased from 48 to 35, and the 45-54 age group also saw a decrease from 34 to 28. Toyota, Honda, and Ford remained the top three vehicle makes involved in crashes, all showing a decrease in their respective counts.
Top Vehicle Makes (178 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Vehicle unit records
31 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (171 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in the 25 mph zone decreased significantly from 19 in October 2021 to 7 in October 2022, while crashes in the 65 mph zone also decreased from 15 to 9. Conversely, crashes in the 10 mph zone increased from 2 to 6, and crashes in the 40 mph zone increased from 9 to 13. All speed zones reported zero fatal crashes in both periods.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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: 2022-10-01 through 2022-10-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-10-01 through 2022-10-31 (31 days)
- Geographic scope: MARLBOROUGH, MA
- Total crash records analyzed: 88
- Total persons involved: 205
- Total vehicles involved: 178
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: October 2022." Published June 21, 2026. Reporting period: 2022-10-01 to 2022-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/marlborough/october-2022-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: 2022-10-01 – 2022-10-31
Generated: June 21, 2026 · All rights reserved