Monthly Traffic Safety Analysis

34 CRASHES IN
NORTON, MA
DECEMBER 2024

All metrics benchmarked againstDecember 2023

NORTON experienced a slight increase in total crashes in December 2024 compared to December 2023, rising from 33 to 34 crashes, a 3% increase. The most notable shift was a 70% increase in total injuries, which rose from 10 in December 2023 to 17 in December 2024.

34

3.0%was 33

Total Crash Events

0

Persons Killed

17

70.0%was 10

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. 1 crash with unreported severity is not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for NORTON shows a slight upward trend in total crashes, increasing by 3% year-over-year from 33 to 34. More significantly, the number of individuals injured in crashes rose from 10 to 17, representing a 70% increase in total injuries.

2

Hit-and-Run Crashes — December 2024

5.9% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

2

Pedestrians Injured

Prior: 1100.0%

15

Motorists Injured

Prior: 966.7%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · 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 Wednesday with 8 crashes in December 2023 to Monday with 8 crashes in December 2024. The peak hour for crashes also changed, moving from 5 PM with 7 crashes in the prior period to 3 PM with 5 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes or fatalities reported in either December 2023 or December 2024. Crashes resulting in serious injuries increased from 1 (3% of total crashes) to 2 (5.9% of total crashes), while minor injury crashes rose from 3 (9.1%) to 8 (23.5%). Conversely, possible injury crashes decreased from 6 (18.2%) to 2 (5.9%), and crashes with no injuries saw a slight decrease from 22 (66.7%) to 21 (61.8%).

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes5.9%
100.0%prior 1
Minor Injury8minor injury crashes23.5%
166.7%prior 3
Possible Injury2possible injury crashes5.9%
-66.7%prior 6
No Injury21no injury crashes61.8%
-4.5%prior 22

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

The count of crashes where 'No improper driving' was cited increased from 6 to 12 year-over-year. 'Driving too fast for conditions' saw an increase in crash count from 1 to 4. Conversely, 'Other improper action' decreased from 3 crashes to 1, and 'Followed too closely' decreased from 2 crashes to 1.

Officer-Reported Primary Contributing Cause

No improper driving12 (35.3%)100.0%prior 6
Inattention5 (14.7%)0.0%prior 5
Driving too fast for conditions4 (11.8%)
Fatigued/asleep2 (5.9%)
Failed to yield right of way2 (5.9%)
Failure to keep in proper lane or running off road1 (2.9%)
Made an improper turn1 (2.9%)
Disregarded traffic signs, signals, road markings1 (2.9%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (2.9%)
Other improper action1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased from 23 to 12, while crashes in 'Snow' related conditions (including Snow, Snow/Sleet, Snow/Blowing sand, Snow/Snow, Snow/Cloudy) increased from 0 to 10. Crashes on 'Dry' road surfaces decreased from 24 to 14, while crashes on 'Wet' road surfaces increased from 6 to 10, and those on 'Snow' surfaces increased from 0 to 7.

Weather

Clear12 (35.3%)
-47.8%prior 23
Cloudy/Rain4 (11.8%)
Snow/Sleet, hail (freezing rain or drizzle)4 (11.8%)
Clear/Cloudy3 (8.8%)
Clear/Rain2 (5.9%)
Clear/Clear2 (5.9%)
Snow2 (5.9%)
Snow/Blowing sand, snow2 (5.9%)
Snow/Snow1 (2.9%)
Sleet, hail (freezing rain or drizzle)1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight19 (55.9%)
90.0%prior 10
Dark - lighted roadway8 (23.5%)
-38.5%prior 13
Dark - roadway not lighted2 (5.9%)
-66.7%prior 6
Dawn2 (5.9%)
Dusk2 (5.9%)
Dark - unknown roadway lighting1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry14 (41.2%)
-41.7%prior 24
Wet10 (29.4%)
66.7%prior 6
Snow7 (20.6%)
Ice2 (5.9%)
Slush1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The age group 21-25 saw a notable increase in persons involved in crashes, rising from 7 to 14 year-over-year, while the 65+ age group saw a decrease from 9 to 4. In terms of vehicle makes, FORD became the most frequently involved make, increasing from 6 to 8 vehicles, while NISSAN decreased from 7 to 1 and JEEP increased from 2 to 6.

Top Vehicle Makes (54 vehicles)

1
FORD8 (14.8%)
33.3%prior 6
2
JEEP6 (11.1%)
3
TOYOTA6 (11.1%)
0.0%prior 6
4
HONDA5 (9.3%)
-16.7%prior 6
5
CHEVROLET3 (5.6%)
6
MAZDA2 (3.7%)
7
CHRYSLER2 (3.7%)
8
RAM2 (3.7%)
9
LEXUS2 (3.7%)
10
GMC2 (3.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (63 persons with recorded sex)

Female34 (54.0%)
70.0%prior 20
Male29 (46.0%)
-27.5%prior 40

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 30 mph speed limit zone increased from 11 to 19 year-over-year. Crashes in the 40 mph zone decreased from 9 to 7, and the 65 mph zone saw a decrease from 5 to 4 crashes. No fatal crashes were reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · 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: 2024-12-01 through 2024-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-12-01 through 2024-12-31 (31 days)
  • Geographic scope: NORTON, MA
  • Total crash records analyzed: 34
  • Total persons involved: 67
  • Total vehicles involved: 54

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). "NORTON, MA Crash Intelligence Report: December 2024." Published June 21, 2026. Reporting period: 2024-12-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/norton/december-2024-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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