ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BELLINGHAM, 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/bellingham/2025-annual-report
Yearly Traffic Safety Analysis
515 CRASHES IN
BELLINGHAM, MA
2025
In Bellingham, total traffic crashes decreased from 583 in the prior year to 515 in the current period, representing an 11.7% reduction. Despite this overall decline in collisions, the number of fatalities doubled from one to two. The most notable year-over-year shift was a significant increase in crashes attributed to 'Followed too closely,' which rose from 26 to 61 incidents.
515
▼ -11.7%was 583
Total Crash Events
2
▲ 100.0%was 1
Persons Killed
139
▼ -2.1%was 142
Persons Injured
22
▲ 15.8%was 19
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. 6 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
Traffic crashes in Bellingham showed a downward trend, with total incidents falling by 11.7% from 583 to 515 year-over-year. While the number of total injuries remained nearly stable, decreasing from 142 to 139, the number of fatalities increased from one to two persons. This indicates that while crash frequency has decreased, the severity of some incidents has worsened.
22
Hit-and-Run Crashes — 2025
▲ 15.8% vs prior (19)
The number of hit-and-run crashes increased from 19 in the prior year to 22 in the current year. This represents an upward trend in the hit-and-run rate, which rose from 3.3% of all crashes in the prior period to 4.3% in the current period.
Vulnerable Road User Casualties
1
Pedestrians Killed
1
Cyclists Killed
0
Motorists Killed
0
Other Killed
3
Pedestrians Injured
3
Cyclists Injured
132
Motorists Injured
1
Other 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 remained consistent, with Monday being the peak day and the 4 p.m. hour being the peak time for collisions in both periods. However, the volume of crashes during these peaks declined year-over-year. The number of crashes on Mondays fell from 108 to 88, and incidents during the 4 p.m. hour decreased from 59 to 46.
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
While total crashes decreased, the number of fatal crashes doubled from one to two, increasing the fatal crash rate from 0.2% to 0.4% of all incidents. The proportion of crashes resulting in minor injuries was stable at 10.1% for both periods. Crashes involving possible injuries increased as a share of the total, rising from 5.3% to 7.0%, while the share of serious injury crashes saw a slight decrease from 1.9% to 1.7%.
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 cited factor in both periods, there were significant shifts in the rankings of other contributing factors. The count of crashes attributed to 'Failed to yield right of way' increased from 46 to 73, a 58.7% rise, making it the second-ranked factor. 'Followed too closely' crashes more than doubled, increasing by 134.6% from 26 to 61 incidents. In contrast, crashes involving 'Inattention' decreased by 44.2% from 52 to 29 incidents.
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
The majority of crashes in both years occurred during daylight hours on dry roads. The proportion of crashes on dry surfaces remained steady at approximately 78%. There was a minor shift in lighting conditions, with the share of crashes occurring in daylight decreasing from 74.3% to 71.1%, while the proportion of crashes on dark, lighted roadways increased from 17.3% to 19.2%.
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
Toyota, Ford, and Honda were the top three vehicle makes involved in crashes during both periods, with their rankings unchanged. However, the number of Ford vehicles in crashes decreased from 157 to 102. A review of person demographics shows a decrease in the involvement of older individuals; the 65+ age group accounted for 12.5% of persons in the prior period but only 9.6% in the current period.
Top Vehicle Makes (974 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
61 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,148 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
Crashes decreased in the most common speed zones, with incidents in 25 mph zones falling from 217 to 178 and in 35 mph zones from 176 to 141. The location of fatal crashes shifted; the prior year's single fatality occurred in a 35 mph zone, whereas the current year's two fatalities occurred in 25 mph and 45 mph zones.
Fatal crashes by zone: 25 mph: 1 of 178 (0.562%) · 45 mph: 1 of 27 (3.704%)
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: BELLINGHAM, MA
- Total crash records analyzed: 515
- Total persons involved: 1,213
- Total vehicles involved: 974
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). "BELLINGHAM, 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/bellingham/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