ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · TYNGSBOROUGH, 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/tyngsborough/2022-annual-report
Yearly Traffic Safety Analysis
292 CRASHES IN
TYNGSBOROUGH, MA
2022
In Tyngsborough, total traffic crashes increased by 3.5%, from 282 incidents in 2021 to 292 in 2022. The most significant year-over-year change was the increase in crash severity, with two traffic fatalities recorded in 2022 compared to zero in the prior year. The total number of injuries also rose from 68 to 95.
292
▲ 3.5%was 282
Total Crash Events
2
Persons Killed
95
▲ 39.7%was 68
Persons Injured
18
▼ -14.3%was 21
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. 7 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
The overall trend shows an increase in crashes and their severity year-over-year. Total collisions rose by 3.5% from 282 to 292. More significantly, the number of persons injured increased by 39.7% from 68 to 95, and two fatalities were recorded in 2022 after none in 2021.
18
Hit-and-Run Crashes — 2022
▼ -14.3% vs prior (21)
Hit-and-run incidents showed a downward trend between the two periods. The total number of hit-and-run crashes decreased from 21 in 2021 to 18 in 2022. Consequently, the hit-and-run rate, representing the share of total crashes that were hit-and-runs, also declined from 7.4% to 6.2%.
Vulnerable Road User Casualties
1
Pedestrians Killed
1
Motorists Killed
1
Pedestrians Injured
94
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
Temporal crash patterns were largely stable between the two periods. Friday remained the peak day for crashes in both 2021 (54 crashes) and 2022 (52 crashes). The peak hour for collisions shifted two hours earlier, from 5 p.m. in 2021 to 3 p.m. in 2022, though both peak hours recorded an identical 27 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 in 2022, with two fatal crashes recorded compared to none in 2021, raising the fatal crash rate from 0% to 0.7%. The proportion of crashes involving any injury rose from 19.9% of all crashes in 2021 to 23.0% in 2022. While the count of 'Serious Injury' crashes decreased from 7 to 3, the number of 'Minor Injury' crashes grew from 33 to 47.
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
In both years, the most common primary factor listed was 'No improper driving,' with its count increasing from 85 in 2021 to 105 in 2022. 'Inattention' remained the second-most cited factor, with its count holding steady at 54 and 52, respectively. Crashes attributed to 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' more than doubled in count, rising from 8 incidents in 2021 to 18 in 2022, a 125% increase in count.
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
In both periods, most crashes occurred in clear weather and daylight. The proportion of crashes on clear days increased from 69.9% in 2021 to 74.3% in 2022. However, there was a notable increase in crashes on adverse road surfaces; incidents on icy roads increased from 4 to 15, and crashes on wet roads rose from 40 to 53.
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 were consistent, with Honda, Toyota, and Ford leading in both years; Honda (83 vehicles) surpassed Toyota (82 vehicles) for the top spot in 2022. A demographic shift occurred among persons involved in crashes, with the 26-34 age group growing from 79 individuals in 2021 to 100 in 2022 to become the largest cohort.
Top Vehicle Makes (487 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
39 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (546 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 35 mph speed zone was the site of the most crashes in both 2021 (95 crashes) and 2022 (91 crashes). A key change was the appearance of fatal crashes in 2022, with one fatality occurring in a 35 mph zone and another in a 55 mph zone; no fatalities were recorded in 2021. Crashes in 30 mph zones saw a notable increase, rising from 35 incidents in 2021 to 48 in 2022.
Fatal crashes by zone: 35 mph: 1 of 91 (1.099%) · 55 mph: 1 of 43 (2.326%)
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: TYNGSBOROUGH, MA
- Total crash records analyzed: 292
- Total persons involved: 605
- Total vehicles involved: 487
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). "TYNGSBOROUGH, 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/tyngsborough/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