ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · WINCHENDON, MA · 2024
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/winchendon/2024-annual-report
Yearly Traffic Safety Analysis
179 CRASHES IN
WINCHENDON, MA
2024
In 2024, Winchendon recorded 179 total vehicle crashes, a 10.1% decrease from the 199 crashes recorded in 2023. While overall crashes and injuries declined, the number of crashes involving a driver suspected of being under the influence of alcohol more than doubled, increasing from 6 in 2023 to 14 in 2024.
179
▼ -10.1%was 199
Total Crash Events
1
Persons Killed
47
▼ -24.2%was 62
Persons Injured
1
▼ -83.3%was 6
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 2 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in traffic collisions was downward year-over-year. Total crashes fell by 10.1% from 199 to 179, and the number of people injured in these incidents decreased by 24.2%, from 62 to 47. The number of fatalities remained unchanged, with one person killed in each period.
1
Hit-and-Run Crashes — 2024
▼ -83.3% vs prior (6)
Hit-and-run incidents saw a substantial decrease year-over-year. The number of crashes classified as hit-and-run fell from 6 in 2023 to 1 in 2024. Consequently, the hit-and-run rate, as a percentage of all crashes, dropped from 3.0% to 0.6%.
Vulnerable Road User Casualties
1
Motorists Killed
0
Other Killed
46
Motorists Injured
1
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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 some consistency and some change between the two periods. Friday remained the peak day for crashes in both years, though the count decreased from 36 to 32. The daily peak hour for collisions shifted earlier, from the 5 p.m. hour in 2023 (19 crashes) to the 3 p.m. hour in 2024 (15 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The number of fatal crashes was stable at one in both 2023 and 2024, resulting in a slight increase in the fatal crash rate from 0.50% to 0.56% due to the lower overall crash total. There was a significant drop in serious injury crashes, from 6 in the prior year to 1 in the current year. The proportion of crashes that resulted in no injuries increased from 75.9% in 2023 to 77.7% in 2024.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record
Top Contributing Factors
Comparing contributing factors, crashes attributed to "Failed to yield right of way" increased in count from 23 to 29, and incidents involving "Inattention" rose from 15 to 22. This represents a 26.1% and 46.7% increase in the count for these factors, respectively. Conversely, the number of crashes where "No improper driving" was cited as a factor decreased from 82 in 2023 to 55 in 2024.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across environmental conditions remained largely consistent year-over-year. The proportion of collisions occurring on dry roads was similar, at 72.1% in 2024 compared to 69.3% in 2023. Crashes in dark conditions, including both lighted and unlighted roadways, accounted for 32.4% of incidents in the current period, an increase from their 27.6% share in the prior period.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
The mix of vehicles involved in crashes saw a shift in the top makes. Ford became the most frequently involved make with 46 vehicles in 2024, compared to 2023 when Toyota was first with 52 vehicles. The 26-34 and 35-44 age groups remained the most represented demographics among persons involved in crashes in both periods, though their total counts decreased from 73 to 59 and 64 to 56, respectively.
Top Vehicle Makes (276 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
17 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (336 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crash locations shifted toward lower speed zones in the current period. The 30 mph zone became the most common site for crashes with 43 incidents, up from 37 in the prior year. In contrast, crashes in the 35 mph zone decreased from 43 to 32. The single fatal crash in 2024 occurred in a 40 mph zone, whereas the prior year's fatal crash was in a 45 mph zone.
Fatal crashes by zone: 40 mph: 1 of 28 (3.571%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: WINCHENDON, MA
- Total crash records analyzed: 179
- Total persons involved: 351
- Total vehicles involved: 276
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). "WINCHENDON, MA Crash Intelligence Report: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/winchendon/2024-annual-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: 2024-01-01 – 2024-12-31
Generated: June 21, 2026 · All rights reserved