Monthly Traffic Safety Analysis

26 CRASHES IN
SOUTHBOROUGH, MA
NOVEMBER 2022

All metrics benchmarked againstNovember 2021

In November 2022, SOUTHBOROUGH experienced 26 crashes, a decrease of 18.75% compared to the 32 crashes recorded in November 2021. Despite the overall reduction in crashes, the number of hit-and-run incidents rose from 0 to 2, while crashes attributed to 'Inattention' increased from 2 to 7.

26

▼ -18.8%was 32

Total Crash Events

0

Persons Killed

10

▲ 11.1%was 9

Persons Injured

2

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, the total number of crashes in November 2022 decreased by 18.75%, with 26 crashes compared to 32 in the prior year. This indicates a positive trend in reducing the total number of traffic incidents year-over-year.

2

Hit-and-Run Crashes — November 2022

7.7% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

10

Motorists Injured

Prior: 8↑25.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · 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 Tuesday in November 2021, with 8 incidents, to Wednesday in November 2022, which saw 11 crashes. The peak hour for crashes remained consistent at 5 PM in both periods, with 6 crashes occurring during this hour in both November 2021 and November 2022.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at 0 in both November 2021 and November 2022. Total injuries increased slightly from 9 to 10 persons, representing an 11.11% rise. The proportion of crashes resulting in minor injuries increased from 12.5% (4 crashes) in the prior period to 23.1% (6 crashes) in the current period, while crashes with no injuries decreased from 81.3% to 73.1%.

Outcome by Severity (Crash Events)

Minor Injury6minor injury crashes23.1%
↑50.0%prior 4
Possible Injury1possible injury crashes3.8%
↓-50.0%prior 2
No Injury19no injury crashes73.1%
↓-26.9%prior 26

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, crashes where 'No improper driving' was cited increased by 3 incidents, rising from 8 in November 2021 to 11 in November 2022. Crashes attributed to 'Inattention' saw a substantial increase, growing from 2 incidents in the prior period to 7 in the current period, a 250% rise in count. Conversely, crashes due to 'Followed too closely' decreased from 4 to 1, and 'Distracted' crashes decreased from 3 to 1.

Officer-Reported Primary Contributing Cause

No improper driving11 (42.3%)↑37.5%prior 8
Inattention7 (26.9%)
Driving too fast for conditions1 (3.8%)
Failed to yield right of way1 (3.8%)
Failure to keep in proper lane or running off road1 (3.8%)
Fatigued/asleep1 (3.8%)
Followed too closely1 (3.8%)
Disregarded traffic signs, signals, road markings1 (3.8%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (3.8%)
Distracted1 (3.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The number of crashes occurring in 'Clear' weather conditions decreased from 27 to 19, while crashes during 'Rain' increased from 1 to 4. Crashes in 'Daylight' conditions significantly decreased from 21 to 10, whereas crashes in 'Dark - lighted roadway' conditions doubled from 6 to 12. Similarly, crashes on 'Dry' road surfaces decreased from 30 to 21, while those on 'Wet' surfaces increased from 2 to 5.

Weather

Clear19 (73.1%)
↓-29.6%prior 27
Rain4 (15.4%)
Cloudy3 (11.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Weather condition at time of crash

Lighting

Dark - lighted roadway12 (46.2%)
↑100.0%prior 6
Daylight10 (38.5%)
↓-52.4%prior 21
Dark - roadway not lighted3 (11.5%)
Dusk1 (3.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Lighting condition field

Road Surface

Dry21 (80.8%)
↓-30.0%prior 30
Wet5 (19.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (44 vehicles)

1
TOYOTA8 (18.2%)
↓-20.0%prior 10
2
FORD6 (13.6%)
↑20.0%prior 5
3
HONDA4 (9.1%)
↓-20.0%prior 5
4
VOLKSWAGEN3 (6.8%)
5
AUDI3 (6.8%)
6
CHEVROLET2 (4.5%)
↓-77.8%prior 9
7
NISSAN2 (4.5%)
↓-60.0%prior 5
8
SUBARU2 (4.5%)
9
GMC2 (4.5%)
10
BMW2 (4.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Vehicle unit records

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

Sex Distribution (49 persons with recorded sex)

Male28 (57.1%)
↓-30.0%prior 40
Female21 (42.9%)
↓-27.6%prior 29

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 40 mph speed zones doubled from 3 in November 2021 to 6 in November 2022. Conversely, crashes in 50 mph zones decreased by 3 incidents, from 12 to 9, and crashes in 65 mph zones decreased from 6 to 5. There were no fatal crashes reported in any speed limit zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · 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: 2022-11-01 through 2022-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-11-01 through 2022-11-30 (30 days)
  • Geographic scope: SOUTHBOROUGH, MA
  • Total crash records analyzed: 26
  • Total persons involved: 52
  • Total vehicles involved: 44

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). "SOUTHBOROUGH, MA Crash Intelligence Report: November 2022." Published June 21, 2026. Reporting period: 2022-11-01 to 2022-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/southborough/november-2022-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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Southborough, MA Crash Report — November 2022 | ThatCarHitMe.com