ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · WEBSTER, MA · 2022
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/2022-annual-report
Yearly Traffic Safety Analysis
366 CRASHES IN
WEBSTER, MA
2022
In Webster, total traffic crashes increased by 3.7% from 353 in 2021 to 366 in 2022. During this period, total injuries rose from 99 to 131. The most significant year-over-year change was the emergence of crash-related fatalities, which increased from zero in the prior year to five in the current year.
366
▲ 3.7%was 353
Total Crash Events
5
Persons Killed
131
▲ 32.3%was 99
Persons Injured
11
▲ 1000.0%was 1
Hit-and-Run Crashes
Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 12 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash data indicates an upward trend in both frequency and severity. Total collisions rose from 353 to 366 year-over-year, while the number of people injured increased by 32.3% from 99 to 131. Most notably, after recording zero fatalities in 2021, there were 5 fatalities resulting from 4 fatal crashes in 2022.
11
Hit-and-Run Crashes — 2022
▲ 1000.0% vs prior (1)
Hit-and-run incidents increased substantially between the two periods. The number of hit-and-run crashes rose from 1 in 2021 to 11 in 2022. Correspondingly, the hit-and-run rate, representing the number of such incidents per 100 crashes, increased from 0.3 to 3.0.
Vulnerable Road User Casualties
1
Pedestrians Killed
1
Cyclists Killed
3
Motorists Killed
0
Other Killed
7
Pedestrians Injured
2
Cyclists Injured
121
Motorists Injured
1
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes saw a slight shift between the two periods. The peak day for crashes moved from Wednesday (66 crashes) in 2021 to Friday (69 crashes) in 2022. Similarly, the peak hour for collisions shifted an hour earlier, from 4 p.m. in the prior year (39 crashes) to 3 p.m. in the current year (36 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity increased significantly year-over-year, with the fatal crash rate rising from 0% to 1.1% of all crashes. While the count of serious injury crashes decreased from 8 to 5, crashes resulting in minor injuries increased from 45 to 60, and possible injury crashes grew from 25 to 36. Consequently, the share of crashes with no reported injuries fell from 72.8% in 2021 to 68% in 2022.
Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
Inattention remained the top contributing factor in both periods, with its count increasing by 16.4% from 61 crashes in 2021 to 71 in 2022. The second-most cited factor shifted; crashes attributed to erratic or reckless driving decreased by 21.4% (from 28 to 22), while crashes involving failure to yield the right of way increased by 15.8% (from 19 to 22).
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across different environmental conditions remained largely stable year-over-year. Crashes in clear weather (74.3% vs 75.9%) and on dry roads (79.2% vs 79.6%) made up a consistent majority in both 2022 and 2021, respectively. There was a minor decrease in the proportion of crashes occurring in dark but lighted roadway conditions, which accounted for 17.2% of crashes in 2022 compared to 20.1% in 2021.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The top makes of vehicles involved in crashes saw a shift in rankings. While Toyota remained the most common make (110 vehicles vs. 94), Honda moved up to the second position (72 vehicles) and Ford moved down to third (70 vehicles). Among persons involved in crashes, there was a notable increase in the 65+ age group, which grew from 71 individuals in 2021 to 86 in 2022.
Top Vehicle Makes (659 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
91 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (723 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
The 30 mph speed zone continued to be the most frequent site for crashes, with the count increasing from 173 in 2021 to 186 in 2022. A significant change was the appearance of fatal crashes within specific speed zones. In 2022, one fatal crash occurred in a 30 mph zone and another in a 55 mph zone, whereas no fatal crashes were recorded in any speed zone in the prior year.
Fatal crashes by zone: 30 mph: 1 of 186 (0.538%) · 55 mph: 1 of 1 (100%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: WEBSTER, MA
- Total crash records analyzed: 366
- Total persons involved: 825
- Total vehicles involved: 659
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: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/webster/2022-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: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved