Monthly Traffic Safety Analysis

52 CRASHES IN
BELLINGHAM, MA
SEPTEMBER 2023

All metrics benchmarked againstSeptember 2022

In September 2023, Bellingham experienced 52 crashes, a decrease of 11.9% compared to the 59 crashes in September 2022. The most notable year-over-year shift was a significant reduction in total injuries, which fell by 86.4% from 22 in the prior period to 3 in the current period.

52

-11.9%was 59

Total Crash Events

0

Persons Killed

3

-86.4%was 22

Persons Injured

3

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. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a decrease in crash incidents year-over-year, with total crashes falling from 59 in September 2022 to 52 in September 2023. This represents an 11.9% reduction in crashes during the current period.

3

Hit-and-Run Crashes — September 2023

0.0% vs prior (3)

The number of hit-and-run crashes remained stable at 3 incidents in both September 2022 and September 2023. However, the hit-and-run rate increased slightly from 5.1% in the prior period to 5.8% in the current period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

3

Motorists Injured

Prior: 22-86.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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 Friday with 13 incidents in September 2022 to Monday with 15 incidents in September 2023. The peak hour for crashes also changed, moving from 3 PM with 9 incidents in the prior period to 2 PM with 6 incidents in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While both periods reported zero fatalities, total injuries saw a substantial decrease from 22 in September 2022 to 3 in September 2023. The proportion of crashes resulting in any injury decreased from 23.7% in the prior period to 5.8% in the current period, with no serious injuries reported in September 2023 compared to one in the prior period.

Outcome by Severity (Crash Events)

Minor Injury1minor injury crashes1.9%
-85.7%prior 7
Possible Injury2possible injury crashes3.8%
-66.7%prior 6
No Injury47no injury crashes90.4%
6.8%prior 44

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record

Top Contributing Factors

Analysis of contributing factors shows a decrease in several categories; 'Inattention' crashes decreased from 15 to 5, and 'Followed too closely' incidents decreased from 7 to 5. Conversely, 'Driving too fast for conditions' incidents increased from 0 to 3, and 'Distracted' incidents rose from 1 to 2.

Officer-Reported Primary Contributing Cause

No improper driving12 (23.1%)0.0%prior 12
Followed too closely5 (9.6%)-28.6%prior 7
Inattention5 (9.6%)-66.7%prior 15
Failed to yield right of way5 (9.6%)-16.7%prior 6
Driving too fast for conditions3 (5.8%)
Distracted2 (3.8%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (3.8%)
Other improper action2 (3.8%)
Failure to keep in proper lane or running off road1 (1.9%)
Over-correcting/over-steering1 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

A notable shift occurred in road surface conditions, with dry conditions decreasing from 55 crashes in September 2022 to 27 crashes in September 2023, while wet conditions increased from 3 crashes to 24 crashes. Correspondingly, crashes in clear weather decreased from 46 to 26, while those in rain conditions increased from 2 to 8.

Weather

Clear26 (50.0%)
-43.5%prior 46
Rain8 (15.4%)
Cloudy/Rain6 (11.5%)
Rain/Cloudy4 (7.7%)
Cloudy4 (7.7%)
-20.0%prior 5
Rain/Fog, smog, smoke1 (1.9%)
Clear/Cloudy1 (1.9%)
-80.0%prior 5
Clear/Fog, smog, smoke1 (1.9%)
Cloudy/Fog, smog, smoke1 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash

Lighting

Daylight41 (78.8%)
-12.8%prior 47
Dark - lighted roadway7 (13.5%)
-12.5%prior 8
Dark - roadway not lighted3 (5.8%)
Dusk1 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field

Road Surface

Dry27 (52.9%)
-50.9%prior 55
Wet24 (47.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 115 in September 2022 to 98 in September 2023. While Toyota, Ford, Honda, Nissan, and Chevrolet remained among the top makes involved, their individual counts generally decreased year-over-year. The age group 65+ saw a decrease in persons involved, from 22 to 15, while the 16-20 age group saw a slight increase from 13 to 15.

Top Vehicle Makes (98 vehicles)

1
TOYOTA18 (18.4%)
-14.3%prior 21
2
FORD10 (10.2%)
-28.6%prior 14
3
HONDA9 (9.2%)
-18.2%prior 11
4
NISSAN8 (8.2%)
-20.0%prior 10
5
CHEVROLET8 (8.2%)
-42.9%prior 14
6
JEEP6 (6.1%)
0.0%prior 6
7
HYUNDAI4 (4.1%)
8
GMC4 (4.1%)
9
VOLVO3 (3.1%)
10
VOLKSWAGEN3 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records

10 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (105 persons with recorded sex)

Male60 (57.1%)
-7.7%prior 65
Female45 (42.9%)
-23.7%prior 59

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 25 mph speed zones decreased from 24 in the prior period to 15 in the current period. Similarly, crashes in 35 mph zones decreased from 18 to 16, and in 40 mph zones from 6 to 2. There was a slight increase in crashes in 65 mph zones, from 2 to 3.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-09-01 through 2023-09-30 (30 days)
  • Geographic scope: BELLINGHAM, MA
  • Total crash records analyzed: 52
  • Total persons involved: 115
  • Total vehicles involved: 98

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: September 2023." Published June 21, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/bellingham/september-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

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Bellingham, MA Crash Report — September 2023 | ThatCarHitMe.com