Monthly Traffic Safety Analysis

65 CRASHES IN
WELLESLEY, MA
NOVEMBER 2024

All metrics benchmarked againstNovember 2023

In November 2024, Wellesley experienced 65 total crashes, an increase of 10.17% compared to the 59 crashes recorded in November 2023. Despite this rise in total crashes, the number of total injuries decreased by 11.76%, from 17 to 15. A notable positive shift was observed in hit-and-run incidents, which decreased by 60% year-over-year.

65

10.2%was 59

Total Crash Events

0

Persons Killed

15

-11.8%was 17

Persons Injured

2

-60.0%was 5

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 · 2024-11-01 to 2024-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a slight increase in total crashes, rising from 59 in November 2023 to 65 in November 2024, a 10.17% increase. Concurrently, total injuries decreased from 17 to 15, marking an 11.76% reduction. There were no fatalities in either period.

2

Hit-and-Run Crashes — November 2024

-60.0% vs prior (5)

Hit-and-run crashes decreased significantly by 60%, from 5 incidents in November 2023 to 2 incidents in November 2024. This reduction resulted in the hit-and-run rate falling from 8.5% in the prior period to 3.1% in the current period.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Cyclists Injured

Prior: 10.0%

14

Motorists Injured

Prior: 16-12.5%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · 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 Thursday in November 2023, which had 12 crashes, to Friday in November 2024, with 15 crashes. The peak hour also changed significantly, moving from 5 PM with 11 crashes in the prior period to 7 AM with 8 crashes in the current period. This suggests a change in the timing of peak crash activity.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The distribution of injury severity shows that serious injuries remained constant at 1 in both November 2023 and November 2024. Minor injuries increased from 4 to 7 year-over-year, while possible injuries decreased from 7 to 4. No fatal crashes or fatalities were recorded in either period.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.5%
0.0%prior 1
Minor Injury7minor injury crashes10.8%
75.0%prior 4
Possible Injury4possible injury crashes6.2%
-42.9%prior 7
No Injury53no injury crashes81.5%
15.2%prior 46

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, 'No improper driving' increased from 14 crashes in November 2023 to 16 crashes in November 2024. 'Followed too closely' saw a significant increase of 116.67%, rising from 6 crashes to 13 crashes. 'Inattention' remained consistent with 11 crashes in both periods.

Officer-Reported Primary Contributing Cause

No improper driving16 (24.6%)14.3%prior 14
Followed too closely13 (20%)116.7%prior 6
Inattention11 (16.9%)0.0%prior 11
Failed to yield right of way8 (12.3%)
Other improper action4 (6.2%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (3.1%)
Made an improper turn1 (1.5%)
Fatigued/asleep1 (1.5%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (1.5%)
Failure to keep in proper lane or running off road1 (1.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions increased from 46 in November 2023 to 56 in November 2024. Similarly, crashes during daylight conditions rose from 33 to 42 year-over-year. Crashes on wet road surfaces tripled, increasing from 3 in the prior period to 9 in the current period.

Weather

Clear56 (87.5%)
21.7%prior 46
Rain3 (4.7%)
Clear/Clear2 (3.1%)
Rain/Cloudy2 (3.1%)
Cloudy1 (1.6%)
-87.5%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Weather condition at time of crash

Lighting

Daylight42 (65.6%)
27.3%prior 33
Dark - lighted roadway17 (26.6%)
6.3%prior 16
Dark - roadway not lighted2 (3.1%)
Dawn2 (3.1%)
Dusk1 (1.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Lighting condition field

Road Surface

Dry54 (83.1%)
-3.6%prior 56
Wet9 (13.8%)
Ice2 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Road surface condition field

Vehicles & Demographics

Toyota remained the most frequently involved vehicle make, with its count increasing from 20 in November 2023 to 30 in November 2024. The age group with the highest number of persons involved shifted from 65+ (20 persons) in the prior period to 45-54 (30 persons) in the current period. The number of males involved increased from 57 to 61, and females increased from 53 to 62.

Top Vehicle Makes (123 vehicles)

1
TOYOTA30 (24.4%)
50.0%prior 20
2
HONDA12 (9.8%)
71.4%prior 7
3
FORD9 (7.3%)
-18.2%prior 11
4
NISSAN8 (6.5%)
5
JEEP7 (5.7%)
-22.2%prior 9
6
LEXUS5 (4.1%)
7
VOLVO5 (4.1%)
8
BMW5 (4.1%)
0.0%prior 5
9
CHEVROLET4 (3.3%)
10
AUDI4 (3.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Vehicle unit records

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

Sex Distribution (123 persons with recorded sex)

Female62 (50.4%)
17.0%prior 53
Male61 (49.6%)
7.0%prior 57

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 MPH speed zones decreased slightly from 30 in November 2023 to 28 in November 2024. Conversely, crashes in 50 MPH speed zones increased from 16 to 24 during the same period. No fatal crashes were reported in any speed zone in either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · 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: 2024-11-01 through 2024-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-11-01 through 2024-11-30 (30 days)
  • Geographic scope: WELLESLEY, MA
  • Total crash records analyzed: 65
  • Total persons involved: 131
  • Total vehicles involved: 123

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). "WELLESLEY, MA Crash Intelligence Report: November 2024." Published June 21, 2026. Reporting period: 2024-11-01 to 2024-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/wellesley/november-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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Wellesley, MA Crash Report — November 2024 | ThatCarHitMe.com