ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · RUTLAND, MA · 2025
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/rutland/2025-annual-report
Yearly Traffic Safety Analysis
120 CRASHES IN
RUTLAND, MA
2025
In Rutland, total traffic crashes increased from 112 in the prior year to 120 in the current year, a 7.1% rise. While total crashes saw a modest increase, the most significant year-over-year change was the emergence of fatal incidents, with two fatalities recorded in the current period compared to none in the prior year. Despite the new fatalities, the total number of individuals injured in crashes decreased substantially from 46 to 17.
120
▲ 7.1%was 112
Total Crash Events
2
Persons Killed
17
▼ -63.0%was 46
Persons Injured
2
▼ -50.0%was 4
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic crash trends in Rutland show a 7.1% increase in total incidents, rising from 112 to 120 year-over-year. This increase was accompanied by a negative shift in crash severity, as the city recorded two fatal crashes in the current period after having none in the previous year. Conversely, the total number of injuries reported dropped by 63%, from 46 in the prior period to 17 in the current period.
2
Hit-and-Run Crashes — 2025
▼ -50.0% vs prior (4)
Hit-and-run incidents decreased in both count and rate compared to the previous year. The number of hit-and-run crashes was halved, falling from 4 in the prior period to 2 in the current period. Consequently, the hit-and-run rate as a percentage of total crashes dropped from 3.6% to 1.7%.
Vulnerable Road User Casualties
0
Pedestrians Killed
2
Motorists Killed
2
Pedestrians Injured
15
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted between the two periods. The peak day for crashes moved from Thursday (21 incidents) in the prior year to Wednesday (25 incidents) in the current year. Similarly, the peak hour for collisions shifted slightly earlier, from the 4 p.m. hour (15 crashes) in the prior period to the 3 p.m. hour (13 crashes) in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes worsened year-over-year, with the introduction of two fatal crashes, which accounted for 1.7% of all incidents in the current period, up from zero in the prior period. However, the overall proportion of crashes involving any level of injury decreased, falling from 23.2% of all crashes in the prior year to 11.7% in the current year. Specifically, crashes resulting in serious injuries dropped from 6 to 4, and minor injury crashes fell from 16 to 9.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record
Top Contributing Factors
While "No improper driving" remained the most common contributing factor in both periods (increasing from 32 to 40 incidents), the prevalence of other factors shifted. Crashes attributed to "Driving too fast for conditions" increased in count by 71.4%, from 7 to 12 incidents. Conversely, crashes involving "Inattention" decreased from 17 to 13, and those from "Failed to yield right of way" fell from 11 to 8.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
There was a notable shift in lighting conditions for crashes year-over-year. The proportion of crashes occurring in daylight decreased from 73.2% to 60.8%, while crashes in unlit, dark roadways increased their share from 12.5% to 22.5%. Road surface conditions showed less variation, with crashes on dry roads remaining stable at approximately 64% of the total in both periods. The share of crashes on wet roads increased from 9.8% to 16.7%.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes remained largely consistent, with Ford (28 vehicles) and Toyota (25 vehicles) being the most frequent in the current period, a slight change from the prior period's ranking of Toyota (24) and Ford (22). A more significant demographic shift occurred among persons involved in crashes; the 16-20 age group's involvement dropped from 39 individuals to 21. Meanwhile, the number of individuals in the 55-64 and 65+ age groups involved in crashes increased from 27 to 32 and 29 to 32, respectively.
Top Vehicle Makes (178 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
10 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (185 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones shifted year-over-year. The two fatal crashes in the current period both occurred in a 45 mph zone, whereas no fatal crashes were recorded in the prior period. Crashes in the 50 mph zone decreased from 33 to 25, while incidents in 30 mph zones increased from 18 to 24 and in 35 mph zones from 16 to 22. This indicates a shift in crash volume from the highest speed zones to mid-range speed zones.
Fatal crashes by zone: 45 mph: 2 of 14 (14.286%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-01-01 through 2025-12-31 (365 days)
- Geographic scope: RUTLAND, MA
- Total crash records analyzed: 120
- Total persons involved: 197
- 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). "RUTLAND, MA Crash Intelligence Report: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/rutland/2025-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: 2025-01-01 – 2025-12-31
Generated: June 21, 2026 · All rights reserved