Monthly Traffic Safety Analysis

116 CRASHES IN
FRAMINGHAM, MA
NOVEMBER 2023

All metrics benchmarked againstNovember 2022

In November 2023, FRAMINGHAM experienced 116 total crashes, a 20% decrease compared to the 145 crashes reported in November 2022. The most significant year-over-year shift was in fatalities, which dropped from 2 in November 2022 to 0 in November 2023.

116

-20.0%was 145

Total Crash Events

0

-100.0%was 2

Persons Killed

40

-13.0%was 46

Persons Injured

14

-17.6%was 17

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 · 2023-11-01 to 2023-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in FRAMINGHAM showed a downward trend year-over-year, with total crashes decreasing by 20% from 145 to 116. Fatalities decreased from 2 to 0, and total injuries also saw a reduction from 46 to 40 in November 2023 compared to the prior year.

14

Hit-and-Run Crashes — November 2023

-17.6% vs prior (17)

The number of hit-and-run crashes decreased from 17 in November 2022 to 14 in November 2023. Despite the decrease in count, the hit-and-run rate slightly increased from 11.7% to 12.1% of total crashes year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1-100.0%

0

Motorists Killed

Prior: 1-100.0%

0

Other Killed

Prior: 00.0%

1

Pedestrians Injured

Prior: 10.0%

38

Motorists Injured

Prior: 45-15.6%

1

Other Injured

Prior: 0%

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 temporal distribution of crashes shifted year-over-year. In November 2023, the peak day for crashes was Wednesday with 21 incidents, while in November 2022, Tuesday was the peak day with 29 crashes. The peak hour also changed from 5 PM with 19 crashes in the prior year to 1 PM with 15 crashes in the current period.

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

Fatalities decreased from 2 in November 2022 to 0 in November 2023, leading to a fatal crash rate reduction from 1.38% to 0%. Total injuries decreased from 46 to 40. The number of minor injury crashes (severity B) decreased from 16 to 13, and possible injury crashes (severity C) decreased from 17 to 15.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes2.6%
Minor Injury13minor injury crashes11.2%
-18.8%prior 16
Possible Injury15possible injury crashes12.9%
-11.8%prior 17
No Injury83no injury crashes71.6%
-21.0%prior 105

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

Several contributing factors saw decreases in crash counts year-over-year. Crashes attributed to 'No improper driving' decreased from 40 to 38, 'Followed too closely' decreased from 21 to 16, and 'Failed to yield right of way' decreased from 17 to 11. Conversely, crashes involving 'Distracted' driving increased from 3 to 4, and 'Inattention' increased from 4 to 5.

Officer-Reported Primary Contributing Cause

No improper driving38 (32.8%)-5.0%prior 40
Followed too closely16 (13.8%)-23.8%prior 21
Failed to yield right of way11 (9.5%)-35.3%prior 17
Failure to keep in proper lane or running off road7 (6%)-30.0%prior 10
Disregarded traffic signs, signals, road markings6 (5.2%)-40.0%prior 10
Other improper action5 (4.3%)0.0%prior 5
Inattention5 (4.3%)
Distracted4 (3.4%)
Made an improper turn3 (2.6%)
Driving too fast for conditions3 (2.6%)

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 number of crashes occurring in 'Clear' weather conditions decreased from 51 in November 2022 to 32 in November 2023, and 'Clear/Clear' conditions decreased from 69 to 61. Crashes during 'Dark - lighted roadway' conditions decreased from 53 to 32. However, crashes in 'Rain' conditions increased from 2 to 7, and crashes on 'Wet' road surfaces increased from 13 to 16.

Weather

Clear/Clear61 (52.6%)
-11.6%prior 69
Clear32 (27.6%)
-37.3%prior 51
Rain7 (6.0%)
Cloudy/Cloudy4 (3.4%)
Cloudy4 (3.4%)
-42.9%prior 7
Rain/Cloudy3 (2.6%)
Rain/Rain3 (2.6%)
-50.0%prior 6
Clear/Cloudy1 (0.9%)
Sleet, hail (freezing rain or drizzle)1 (0.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Weather condition at time of crash

Lighting

Daylight74 (63.8%)
10.4%prior 67
Dark - lighted roadway32 (27.6%)
-39.6%prior 53
Dark - roadway not lighted5 (4.3%)
-44.4%prior 9
Dawn4 (3.4%)
Dusk1 (0.9%)
-87.5%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Lighting condition field

Road Surface

Dry99 (85.3%)
-24.4%prior 131
Wet16 (13.8%)
23.1%prior 13
Slush1 (0.9%)

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 decreased from 281 to 228 year-over-year. Among top vehicle makes, Toyota saw a slight decrease from 42 to 39, while Honda remained stable with 31 vehicles involved in both periods. There was a notable decrease in persons aged 0-15 involved in crashes, from 22 to 7, and a slight increase in persons aged 65+ involved, from 23 to 27.

Top Vehicle Makes (228 vehicles)

1
TOYOTA39 (17.1%)
-7.1%prior 42
2
HONDA31 (13.6%)
0.0%prior 31
3
FORD29 (12.7%)
-12.1%prior 33
4
NISSAN12 (5.3%)
-36.8%prior 19
5
CHEVROLET12 (5.3%)
-45.5%prior 22
6
JEEP11 (4.8%)
-15.4%prior 13
7
SUBARU10 (4.4%)
42.9%prior 7
8
MAZDA9 (3.9%)
28.6%prior 7
9
HYUNDAI8 (3.5%)
-27.3%prior 11
10
BMW7 (3.1%)
-22.2%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Vehicle unit records

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

Sex Distribution (239 persons with recorded sex)

Male141 (59.0%)
-23.4%prior 184
Female97 (40.6%)
-26.0%prior 131
X / Unspecified1 (0.4%)
-50.0%prior 2

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 occurring in 65 MPH speed zones decreased from 24 in November 2022 to 14 in November 2023, with no fatalities reported in either period for this zone. Crashes in 25 MPH speed zones decreased from 7 to 5, and fatalities in this zone decreased from 1 to 0. Crashes in 35 MPH speed zones increased from 2 to 4, with no fatalities reported in 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: FRAMINGHAM, MA
  • Total crash records analyzed: 116
  • Total persons involved: 268
  • Total vehicles involved: 228

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). "FRAMINGHAM, 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/framingham/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

Framingham, MA Crash Report — November 2023 | ThatCarHitMe.com