ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · ATTLEBORO, 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/attleboro/november-2023-report
Monthly Traffic Safety Analysis
104 CRASHES IN
ATTLEBORO, MA
NOVEMBER 2023
ATTLEBORO experienced a slight decrease in total crashes, from 105 in November 2022 to 104 in November 2023, representing a 1.0% reduction. Despite this, total injuries increased by 25%, from 24 to 30. The most notable shift was a 200% increase in crashes involving speeding, rising from 3 to 9 incidents.
104
▼ -1.0%was 105
Total Crash Events
0
Persons Killed
30
▲ 25.0%was 24
Persons Injured
6
▲ 50.0%was 4
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.
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
Overall crash incidents remained relatively stable year-over-year, with a minor decrease of 1 crash from 105 to 104, a 1.0% reduction. Fatalities remained at 0 in both periods. However, the number of injured persons increased by 25%, rising from 24 in November 2022 to 30 in November 2023.
6
Hit-and-Run Crashes — November 2023
▲ 50.0% vs prior (4)
Hit-and-run crashes increased by 50% year-over-year, rising from 4 incidents in November 2022 to 6 incidents in November 2023. This resulted in the hit-and-run crash rate increasing from 3.8% to 5.8% of all crashes.
Vulnerable Road User Casualties
0
Motorists Killed
30
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
The peak day for crashes shifted from Thursday and Saturday (19 crashes each) in November 2022 to Wednesday (21 crashes) in November 2023. Crashes on Wednesday increased by 90.9% (from 11 to 21), while Friday saw a 50% decrease (from 16 to 8). The peak crash hour also shifted from 5 p.m. (12 crashes) in the prior period to 6 p.m. (10 crashes) in the current period, with crashes at 5 p.m. decreasing by 75% (from 12 to 3).
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 fatalities reported in either November 2022 or November 2023. Total injuries increased by 25%, from 24 to 30. While serious injuries remained stable at 2 incidents in both periods, minor injuries decreased by 50% (from 14 to 7), and possible injuries increased by 200% (from 5 to 15).
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
The leading contributing factor shifted from 'Failed to yield right of way' (21 crashes, 20% share) in November 2022 to 'Followed too closely' (19 crashes, 18.3% share) in November 2023. Crashes attributed to 'Followed too closely' increased by 26.7% (from 15 to 19), while 'Failed to yield right of way' decreased by 19% (from 21 to 17). 'Inattention' crashes increased by 45.5% (from 11 to 16), and 'Driving too fast for conditions' increased by 150% (from 2 to 5).
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
The proportion of crashes occurring in clear weather conditions remained consistent, with 86 crashes in November 2023 compared to 85 in November 2022. Crashes during daylight hours increased slightly from 58 to 60. However, crashes occurring in 'Dark - roadway not lighted' conditions increased by 50% (from 12 to 18), while those in 'Dark - lighted roadway' conditions decreased by 50% (from 32 to 16). The number of crashes on wet road surfaces decreased by 30.8% (from 13 to 9), but crashes on roads with 'Water (standing, moving)' increased from 0 to 4.
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 vehicles involved in crashes slightly increased from 188 to 191. While Toyota remained the top vehicle make involved, its count decreased by 15.6% (from 45 to 38). Honda saw a 36.8% increase in involvement (from 19 to 26), moving from third to second place, and Nissan's involvement decreased by 23.8% (from 21 to 16). For persons involved, the 21-25 age group saw a 64.1% decrease (from 39 to 14), while the 35-44 age group experienced an 80% increase (from 25 to 45).
Top Vehicle Makes (191 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Vehicle unit records
8 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (218 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
Crashes in 30 mph zones increased by 27.6% (from 29 to 37), and crashes in 65 mph zones increased by 33.3% (from 24 to 32). Conversely, crashes in 40 mph zones decreased by 41.2% (from 17 to 10). No fatal crashes were recorded in any speed zone during either 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: ATTLEBORO, MA
- Total crash records analyzed: 104
- Total persons involved: 236
- Total vehicles involved: 191
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). "ATTLEBORO, 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/attleboro/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