ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · WEBSTER, MA · 2023
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/2023-annual-report
Yearly Traffic Safety Analysis
327 CRASHES IN
WEBSTER, MA
2023
In 2023, Webster recorded 327 total crashes, a 10.7% decrease from the 366 crashes documented in 2022. While overall collisions declined, the most significant change was a reduction in fatalities, which fell from 5 in the prior year to 2 in the current year.
327
▼ -10.7%was 366
Total Crash Events
2
▼ -60.0%was 5
Persons Killed
123
▼ -6.1%was 131
Persons Injured
14
▲ 27.3%was 11
Hit-and-Run Crashes
Note: "Persons Killed" (2) 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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash data for Webster indicates a downward trend year-over-year. Total collisions fell by 10.7%, from 366 in 2022 to 327 in 2023. This trend included a 6.1% decrease in total injuries (from 131 to 123) and a 60% decrease in fatalities (from 5 to 2).
14
Hit-and-Run Crashes — 2023
▲ 27.3% vs prior (11)
Hit-and-run incidents increased in both absolute count and as a proportion of total crashes. The number of hit-and-run crashes rose from 11 in 2022 to 14 in 2023. Consequently, the hit-and-run rate increased from 3.0% to 4.3% of all crashes year-over-year.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
4
Pedestrians Injured
2
Cyclists Injured
117
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
Temporal patterns show some shifts between the two periods. While Friday remained the peak day for crashes in both 2022 (69 crashes) and 2023 (63 crashes), the peak hour moved later in the day. In 2023, the highest number of crashes occurred during the 5 PM hour (46 crashes), a shift from the 3 PM peak hour (36 crashes) observed in the prior year.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes decreased year-over-year. The number of fatal crashes dropped from 4 in 2022 to 1 in 2023, reducing the fatal crash rate from 1.1% to 0.3% of all collisions. The number of crashes involving serious injuries was unchanged at 5 for both years. The proportion of crashes resulting in any injury remained stable, accounting for 28.1% of crashes in 2023 compared to 27.6% in 2022.
Severity is per crash event (most severe injury). 1 fatal crash events resulted in 2 persons killed.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
While 'Inattention' remained a consistent leading factor with 71 crashes in both 2022 and 2023, there were notable shifts in other driver behaviors. Crashes attributed to 'Failed to yield right of way' increased in count by 40.9%, from 22 to 31. Similarly, crashes involving 'Followed too closely' rose by 72.7% in count, from 11 to 19. The count for 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' also grew from 22 to 27 crashes.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The majority of crashes in both periods occurred in daylight on dry roads. However, there was a shift in the proportion of crashes occurring in adverse weather. In 2023, 12.5% of crashes happened during rain (41 crashes), nearly double the 6.6% share observed in 2022 (24 crashes). Correspondingly, the share of crashes on wet roads increased from 16.1% to 17.7%.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes remained Toyota, Ford, and Honda in both 2022 and 2023. In the current year, Ford (82 vehicles) surpassed Honda (69 vehicles) for the second-most common make, while Toyota remained number one despite its count decreasing from 110 to 94. The age distribution of persons involved in crashes was also consistent, with the 26-34 age group representing the largest share in both periods.
Top Vehicle Makes (589 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
59 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (680 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
There was a noticeable shift in where crashes occurred by speed zone. Collisions in 30 MPH zones decreased from 186 to 154, while crashes in 65 MPH zones increased from 28 to 35. The single fatal crash in 2023 occurred in a 65 MPH zone. This contrasts with the prior year, where fatal crashes were recorded in 30 MPH and 55 MPH zones.
Fatal crashes by zone: 65 mph: 1 of 35 (2.857%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: WEBSTER, MA
- Total crash records analyzed: 327
- Total persons involved: 750
- Total vehicles involved: 589
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: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/webster/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved