ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BOYLSTON, 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/boylston/2023-annual-report
Yearly Traffic Safety Analysis
49 CRASHES IN
BOYLSTON, MA
2023
In 2023, Boylston recorded 49 total traffic crashes, a 25.8% decrease from the 66 crashes reported in 2022. The most significant year-over-year change was the reduction in fatal crashes, which dropped from two in the prior year to zero in the current year. The total number of injuries remained nearly stable, with 14 individuals injured in 2023 compared to 15 in 2022.
49
▼ -25.8%was 66
Total Crash Events
0
▼ -100.0%was 2
Persons Killed
14
▼ -6.7%was 15
Persons Injured
2
▲ 100.0%was 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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Traffic crashes in Boylston showed a significant downward trend year-over-year, falling by 25.8% from 66 incidents in 2022 to 49 in 2023. This represents a net decrease of 17 crashes. While the total number of injuries saw a slight decrease from 15 to 14, the number of fatalities fell from two to zero.
2
Hit-and-Run Crashes — 2023
▲ 100.0% vs prior (1)
The number of hit-and-run incidents increased from one in 2022 to two in 2023. Correspondingly, the hit-and-run rate, which measures the percentage of total crashes that were hit-and-runs, rose from 1.5% in the prior year to 4.1% in the current year. This indicates an upward trend in both the absolute count and the proportional rate of these crashes.
Vulnerable Road User Casualties
0
Motorists Killed
14
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
The temporal patterns of crashes shifted between the two periods. In 2023, the peak day for crashes was Tuesday with 10 incidents, a change from Friday (13 incidents) in 2022. Similarly, the peak hour for crashes moved earlier in the day, from the 5 p.m. hour in 2022 (8 crashes) to the 3 p.m. hour in 2023 (5 crashes).
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
A significant improvement in crash severity was observed, with fatal crashes decreasing from two in 2022 to zero in 2023. The number of crashes resulting in serious injuries remained constant at one incident in both years. However, the proportion of crashes involving any level of injury (serious, minor, or possible) increased from a 16.6% share in 2022 to a 26.5% share in 2023.
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 'No improper driving' was the most cited circumstance in both periods, its count decreased from 25 crashes in 2022 to 19 in 2023. Crashes attributed to 'Inattention' also fell, from a count of 10 to 6 incidents. Conversely, crashes involving 'Distracted' driving saw a notable increase in count, rising from one incident in 2022 to five in 2023.
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 2023 and 2022 occurred in clear weather and on dry road surfaces, with the proportions remaining stable year-over-year. In 2023, 67.3% of crashes happened in clear weather, compared to 65.2% in 2022. A larger share of crashes occurred during daylight hours in 2023 (71.4%) compared to the prior year (60.6%).
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 most common vehicle makes involved in crashes remained consistent, with Toyota, Chevrolet, and Honda being the top three in both years, though in a slightly different order. Analysis of persons involved in crashes shows a shift in age demographics. In 2023, the 26-34 age group was the most represented with 18 individuals, whereas in 2022, the 45-54 age group was the largest cohort with 22 individuals.
Top Vehicle Makes (71 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
1 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (79 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
In 2023, the highest number of crashes occurred in 40 mph zones (13 incidents), followed by 45 mph zones (10 incidents). This contrasts with 2022, where crashes were more evenly distributed across 35, 40, and 45 mph zones, with 15, 15, and 16 crashes respectively. Notably, the two fatal crashes in 2022 occurred in 35 mph and 65 mph zones, while 2023 saw no fatal crashes in any speed zone.
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: BOYLSTON, MA
- Total crash records analyzed: 49
- Total persons involved: 83
- 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). "BOYLSTON, 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/boylston/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