ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · HOPKINTON, MA · JANUARY 2026
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/hopkinton/january-2026-report
Monthly Traffic Safety Analysis
48 CRASHES IN
HOPKINTON, MA
JANUARY 2026
Total crashes in HOPKINTON, MA increased by 45.5% from 33 in January 2025 to 48 in January 2026. Concurrently, total injuries saw a substantial increase of 450%, rising from 2 in January 2025 to 11 in January 2026. This indicates a notable shift towards crashes resulting in injuries.
48
▲ 45.5%was 33
Total Crash Events
0
Persons Killed
11
▲ 450.0%was 2
Persons Injured
4
▲ 300.0%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 · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash activity in HOPKINTON, MA shows an upward trend year-over-year, with total crashes increasing by 45.5% from 33 in January 2025 to 48 in January 2026. This rise was accompanied by a significant 450% increase in total injuries, from 2 to 11, indicating a worsening outcome for those involved in crashes. Fatalities remained at 0 in both periods.
4
Hit-and-Run Crashes — January 2026
▲ 300.0% vs prior (1)
The number of hit-and-run crashes increased from 1 in January 2025 to 4 in January 2026. This led to an increase in the hit-and-run rate from 3% of all crashes in January 2025 to 8.3% in January 2026. The data indicates an upward trend in hit-and-run incidents.
Vulnerable Road User Casualties
0
Motorists Killed
11
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak day for crashes remained Tuesday in both periods, with 7 crashes in January 2025 and 10 crashes in January 2026. However, the peak crash hour shifted from 7 AM with 8 crashes in January 2025 to 5 PM with 6 crashes in January 2026. This suggests a change in the timing of peak crash occurrences.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Total injuries increased substantially from 2 in January 2025 to 11 in January 2026. While January 2025 reported only 2 possible injuries, January 2026 saw 6 minor injuries and 5 possible injuries. Fatalities remained at 0 in both periods, but the share of crashes resulting in any injury increased from 6.1% (2 of 33) to 22.9% (11 of 48).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Most severe injury per crash record
Top Contributing Factors
The contributing factor 'Failure to keep in proper lane or running off road' saw the largest increase in crash count, rising by 6 crashes from 1 in January 2025 to 7 in January 2026. Additionally, 'Driving too fast for conditions' emerged as a factor in January 2026 with 4 crashes, not being listed among the top factors in January 2025. Conversely, 'No improper driving' decreased by 1 crash, from 13 to 12.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in dark conditions significantly increased, with those in 'Dark - lighted roadway' rising from 5 to 12 crashes and 'Dark - roadway not lighted' increasing from 2 to 9 crashes. Crashes on snowy road surfaces also saw a notable rise, from 2 in January 2025 to 10 in January 2026. Furthermore, 'Slush' appeared as a road surface condition in January 2026, contributing to 3 crashes.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 58 in January 2025 to 86 in January 2026. Toyota remained the top vehicle make involved in crashes, increasing from 13 to 16. The 26-34 age group experienced a significant increase in persons involved in crashes, rising from 8 to 21, while the 0-15 age group was present in the prior period with 3 persons but not in the current period.
Top Vehicle Makes (86 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Vehicle unit records
8 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (102 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 65 mph speed zones increased significantly from 3 in January 2025 to 12 in January 2026. Similarly, crashes in 40 mph zones rose from 3 to 10 during the same period. Conversely, crashes in 35 mph zones decreased from 7 to 3. Fatalities remained at 0 across all speed zones in both periods.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · 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: 2026-01-01 through 2026-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2026-01-01 through 2026-01-31 (31 days)
- Geographic scope: HOPKINTON, MA
- Total crash records analyzed: 48
- Total persons involved: 110
- Total vehicles involved: 86
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). "HOPKINTON, MA Crash Intelligence Report: January 2026." Published June 21, 2026. Reporting period: 2026-01-01 to 2026-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/hopkinton/january-2026-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: 2026-01-01 – 2026-01-31
Generated: June 21, 2026 · All rights reserved