Monthly Traffic Safety Analysis

35 CRASHES IN
WEBSTER, MA
FEBRUARY 2024

All metrics benchmarked againstFebruary 2023

The current period (February 2024) saw 35 total crashes in WEBSTER, MA, marking a 25% increase from the 28 crashes reported in February 2023. Total injuries rose significantly by 133.3%, from 3 to 7. Notably, DUI-related crashes decreased by 80%, from 5 crashes to 1.

35

25.0%was 28

Total Crash Events

0

Persons Killed

7

133.3%was 3

Persons Injured

1

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 · 2024-02-01 to 2024-02-29 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for WEBSTER, MA indicates an upward trend year-over-year, with total crashes increasing by 25% from 28 to 35. Injuries also saw a substantial rise of 133.3%, from 3 to 7. Fatalities remained at zero in both periods.

1

Hit-and-Run Crashes — February 2024

2.9% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

7

Motorists Injured

Prior: 3133.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · 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 6 crashes in February 2023 to Tuesday with 10 crashes in February 2024. The peak hour also changed, with 5 PM being a peak with 3 crashes in the prior period, while 4 PM recorded 6 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Total injuries increased by 133.3%, rising from 3 in February 2023 to 7 in February 2024. Minor injuries (severity B) saw an increase from 1 crash (3.6% of total) to 4 crashes (11.4% of total), while possible injuries (severity C) decreased from 2 crashes (7.1% of total) to 1 crash (2.9% of total). Fatalities remained at 0 in both periods.

Outcome by Severity (Crash Events)

Minor Injury4minor injury crashes11.4%
300.0%prior 1
Possible Injury1possible injury crashes2.9%
-50.0%prior 2
No Injury30no injury crashes85.7%
20.0%prior 25

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Most severe injury per crash record

Top Contributing Factors

Inattention emerged as the leading contributing factor in February 2024, increasing by 7 crashes (233.3%) from 3 to 10. Conversely, crashes attributed to 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' decreased by 2 crashes (-50%), from 4 to 2, and 'Failed to yield right of way' also decreased by 2 crashes (-50%), from 4 to 2. 'No improper driving' crashes increased by 2 (40%), from 5 to 7.

Officer-Reported Primary Contributing Cause

Inattention10 (28.6%)
No improper driving7 (20%)40.0%prior 5
Other improper action4 (11.4%)
Failed to yield right of way2 (5.7%)
Glare2 (5.7%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (5.7%)
Failure to keep in proper lane or running off road1 (2.9%)
Distracted1 (2.9%)
Driving too fast for conditions1 (2.9%)
Over-correcting/over-steering1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Snow' weather conditions increased from 2 in February 2023 to 6 in February 2024, while 'Clear' weather crashes slightly decreased from 24 to 23. Road surface conditions also showed a shift, with 'Snow' related crashes rising from 2 to 7, and 'Wet' and 'Ice' conditions appearing in the current period with 3 and 2 crashes respectively, compared to 0 in the prior period. 'Daylight' crashes increased from 17 to 26.

Weather

Clear23 (65.7%)
-4.2%prior 24
Snow6 (17.1%)
Cloudy3 (8.6%)
Cloudy/Blowing sand, snow1 (2.9%)
Rain1 (2.9%)
Snow/Cloudy1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Weather condition at time of crash

Lighting

Daylight26 (74.3%)
52.9%prior 17
Dark - lighted roadway6 (17.1%)
20.0%prior 5
Dark - unknown roadway lighting3 (8.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Lighting condition field

Road Surface

Dry23 (65.7%)
-11.5%prior 26
Snow7 (20.0%)
Wet3 (8.6%)
Ice2 (5.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (71 vehicles)

1
TOYOTA14 (19.7%)
40.0%prior 10
2
HYUNDAI7 (9.9%)
3
NISSAN7 (9.9%)
4
FORD7 (9.9%)
0.0%prior 7
5
CHEVROLET5 (7%)
0.0%prior 5
6
HONDA4 (5.6%)
-20.0%prior 5
7
SUBARU3 (4.2%)
8
JEEP3 (4.2%)
9
DODGE3 (4.2%)
10
OTH2 (2.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Vehicle unit records

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

Sex Distribution (71 persons with recorded sex)

Female37 (52.1%)
27.6%prior 29
Male34 (47.9%)
6.3%prior 32

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Person-level records linked to crash events

Speed Limit Zones

The 30 mph speed zone experienced the largest increase in crash count, rising by 6 crashes from 11 to 17. Crashes in the 35 mph zone decreased by 3 crashes, from 5 to 2. All other speed zones saw smaller changes in crash counts, and no fatalities were reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · 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: 2024-02-01 through 2024-02-29
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-02-01 through 2024-02-29 (29 days)
  • Geographic scope: WEBSTER, MA
  • Total crash records analyzed: 35
  • Total persons involved: 84
  • Total vehicles involved: 71

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). "WEBSTER, MA Crash Intelligence Report: February 2024." Published June 21, 2026. Reporting period: 2024-02-01 to 2024-02-29. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/webster/february-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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Webster, MA Crash Report — February 2024 | ThatCarHitMe.com