ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · HOPKINTON, 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/hopkinton/2022-annual-report
Yearly Traffic Safety Analysis
507 CRASHES IN
HOPKINTON, MA
2022
In 2022, Hopkinton recorded 507 traffic crashes, a 31.4% increase from the 386 crashes reported in 2021. While total reported injuries decreased from 112 to 92, the number of fatal crashes doubled from one to two during the same period. The most significant shift was the overall increase in crash volume, with a corresponding rise in property-damage-only incidents.
507
▲ 31.3%was 386
Total Crash Events
2
▲ 100.0%was 1
Persons Killed
92
▼ -17.9%was 112
Persons Injured
31
▲ 106.7%was 15
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 13 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 trends in Hopkinton show a significant year-over-year increase, with total collisions rising by 31.4% from 386 in 2021 to 507 in 2022. This represents an increase of 121 crashes. Despite this rise in total incidents, the number of reported injuries decreased by 17.9% from 112 to 92, while fatalities increased from one to two.
31
Hit-and-Run Crashes — 2022
▲ 106.7% vs prior (15)
Hit-and-run incidents increased significantly in both count and rate from 2021 to 2022. The number of hit-and-run crashes more than doubled, rising from 15 to 31, which represents a 106.7% increase in count. Correspondingly, the hit-and-run rate, as a percentage of total crashes, climbed from 3.9% in 2021 to 6.1% in 2022.
Vulnerable Road User Casualties
0
Cyclists Killed
2
Motorists Killed
1
Cyclists Injured
91
Motorists 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 remained broadly consistent year-over-year, with the afternoon commute being the primary time for incidents. The peak hour for crashes in both 2022 and 2021 was 4 p.m., with counts increasing from 34 to 51. Friday was the peak day for crashes in both years, with the number of incidents on that day rising from 77 in 2021 to 91 in 2022.
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
While total crashes rose, the overall severity mix shifted towards less severe outcomes. The proportion of crashes resulting in any injury fell from 22.5% in 2021 to 14.2% in 2022, driven by a drop in serious injury crashes (from 10 to 3). Consequently, the share of no-injury crashes increased from 75.1% to 82.8% of all incidents. However, the number of fatal crashes doubled from one to two.
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
The leading contributing factors showed some shifts between the two periods. While 'Inattention' as a factor decreased in count from 64 in 2021 to 53 in 2022, other factors saw significant growth. Crashes attributed to 'Failure to keep in proper lane or running off road' more than doubled, increasing in count from 17 to 41. Similarly, 'Failed to yield right of way' incidents increased from 38 to 50.
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. In both 2022 and 2021, approximately 70% of crashes occurred in clear weather and during daylight hours. The proportion of crashes on non-dry road surfaces saw a slight increase from 22.0% in 2021 to 24.7% in 2022. The overall increase in total crashes was not driven by a significant shift toward more adverse conditions.
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 three vehicle makes involved in crashes—Toyota, Honda, and Ford—remained consistent between 2021 and 2022, with each seeing an increase in counts corresponding to the overall trend. Analysis of persons involved reveals a demographic shift, as the 35-44 age group became the most frequently involved, increasing from 143 individuals in 2021 to 225 in 2022. In contrast, the number of persons aged 0-15 involved in crashes decreased from 94 to 6.
Top Vehicle Makes (881 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
49 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (988 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 distribution of crashes across speed zones remained similar year-over-year, with the 65 mph zone accounting for the highest number of incidents in both periods (141 in 2021 and 162 in 2022). Crashes increased across most posted speed limit zones, reflecting the overall upward trend. A notable change was the location of fatal crashes; while one fatality occurred in a 65 mph zone in both years, 2022 saw an additional fatal crash in a 35 mph zone.
Fatal crashes by zone: 35 mph: 1 of 52 (1.923%) · 65 mph: 1 of 162 (0.617%)
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: HOPKINTON, MA
- Total crash records analyzed: 507
- Total persons involved: 1,055
- Total vehicles involved: 881
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: 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/hopkinton/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