ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · WEBSTER, MA · 2025
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/webster/2025-annual-report
Yearly Traffic Safety Analysis
399 CRASHES IN
WEBSTER, MA
2025
In 2025, Webster recorded 399 total crashes, a 16.7% increase from the 342 crashes documented in 2024. While total fatalities decreased from two to one year-over-year, the number of crashes resulting in serious injuries saw a substantial increase, rising from 2 in the prior period to 11 in the current period. The most frequent crash type also shifted from rear-end collisions in 2024 to angle collisions in 2025.
399
▲ 16.7%was 342
Total Crash Events
1
▼ -50.0%was 2
Persons Killed
130
▲ 15.0%was 113
Persons Injured
19
▲ 26.7%was 15
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 8 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic safety trends in Webster show an increase in crash frequency. The total number of crashes rose by 16.7%, from 342 in 2024 to 399 in 2025. Correspondingly, the number of persons injured increased by 15.0% from 113 to 130, while the number of fatalities fell from two to one.
19
Hit-and-Run Crashes — 2025
▲ 26.7% vs prior (15)
The number of hit-and-run crashes increased from 15 in 2024 to 19 in 2025. This change resulted in a slight increase in the hit-and-run rate as a percentage of total crashes, which edged up from 4.4% to 4.8%. The data indicates a rising trend in both the absolute number and the proportion of hit-and-run incidents year-over-year.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
0
Other Killed
2
Pedestrians Injured
7
Cyclists Injured
117
Motorists Injured
4
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes showed a slight shift between the two periods. The peak day for crashes moved from Thursday in 2024 (65 crashes) to Friday in 2025 (65 crashes), while the peak hour shifted earlier from 4 p.m. to 3 p.m. Weekend crashes occurring on Saturday and Sunday saw a notable increase, rising from a combined 81 incidents in 2024 to 108 in 2025.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While the number of fatal crashes decreased from two in 2024 to one in 2025, the severity of non-fatal injury crashes intensified. The count of serious injury crashes increased from 2 to 11, and their share of all crashes rose from 0.6% to 2.8%. The proportion of crashes resulting in no injuries remained stable, accounting for 72.8% of incidents in 2024 and 72.7% in 2025.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record
Top Contributing Factors
Inattention remained a primary contributing factor in both years, with the count of related crashes increasing from 74 to 77. A significant year-over-year change was observed in crashes attributed to 'Disregarded traffic signs, signals, road markings,' which surged from 1 incident in 2024 to 13 in 2025. Conversely, crashes where 'Followed too closely' was a factor saw a substantial decrease in count, falling from 19 to 7.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The vast majority of crashes in both periods occurred in daylight on dry roads, with proportions remaining relatively stable. In 2025, 72.7% of crashes happened during daylight, compared to 74.3% in 2024. The count of crashes on wet road surfaces increased from 46 to 59, while incidents during snowy weather conditions decreased significantly from 17 in 2024 to 3 in 2025.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field
Vehicles & Demographics
The top four vehicle makes involved in collisions—Toyota, Ford, Honda, and Chevrolet—were consistent across both years. Analysis of persons involved shows the 35-44 age group experienced the largest percentage increase in involvement, rising from 103 individuals in 2024 to 142 in 2025, a 37.9% increase. The number of persons aged 16-20 involved in crashes also grew by 20.8%, from 77 to 93.
Top Vehicle Makes (717 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
89 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (825 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 30 mph zones were the most common in both periods, with counts increasing from 180 to 195. The single fatal crash in 2025 occurred in a 40 mph zone, whereas the two fatalities in 2024 were recorded in 30 mph and 65 mph zones. The total number of crashes in 40 mph zones increased from 13 in 2024 to 21 in 2025.
Fatal crashes by zone: 40 mph: 1 of 21 (4.762%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-01-01 through 2025-12-31 (365 days)
- Geographic scope: WEBSTER, MA
- Total crash records analyzed: 399
- Total persons involved: 928
- Total vehicles involved: 717
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: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/webster/2025-annual-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
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2025-01-01 – 2025-12-31
Generated: June 21, 2026 · All rights reserved