ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · TEWKSBURY, MA · MARCH 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/tewksbury/march-2022-report
Monthly Traffic Safety Analysis
57 CRASHES IN
TEWKSBURY, MA
MARCH 2022
Total crashes in Tewksbury increased by 67.6% year-over-year, rising from 34 in March 2021 to 57 in March 2022. Total injuries also saw a significant increase of 71.4%, from 7 to 12. The most notable year-over-year shift was a 300% increase in hit-and-run crashes, which rose from 1 to 4 incidents.
57
▲ 67.6%was 34
Total Crash Events
0
Persons Killed
12
▲ 71.4%was 7
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. 2 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash data for March 2022 indicates a substantial upward trend compared to March 2021. Total crashes increased by 67.6%, rising from 34 to 57 incidents. This increase was accompanied by a 71.4% rise in total injuries, from 7 to 12.
4
Hit-and-Run Crashes — March 2022
▲ 300.0% vs prior (1)
Hit-and-run crashes increased significantly from 1 incident in March 2021 to 4 incidents in March 2022, representing a 300% rise. The hit-and-run rate also climbed from 2.9% to 7% of all crashes. This data indicates an upward trend in hit-and-run incidents year-over-year.
Vulnerable Road User Casualties
0
Motorists Killed
12
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · 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 year-over-year, with the peak day moving from Friday in March 2021 (7 crashes) to Thursday in March 2022 (15 crashes). The peak crash hour also changed from 12 p.m. (5 crashes) in the prior period to 7 a.m. (9 crashes) in the current period. This suggests a shift in when the highest number of incidents occurred.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes remained at zero in both March 2021 and March 2022. However, total injuries increased from 7 to 12 year-over-year, including the appearance of 1 serious injury in March 2022 where none were recorded previously. The proportion of injury crashes (serious, minor, or possible) remained relatively stable, accounting for 17.6% of crashes in March 2021 and 17.5% in March 2022.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Most severe injury per crash record
Top Contributing Factors
The most frequent contributing factor, 'No improper driving,' increased by 125% from 8 crashes in March 2021 to 18 crashes in March 2022. 'Inattention' also rose from 9 to 13 crashes, a 44.4% increase, though it dropped from the top rank to second. 'Failed to yield right of way' remained constant at 7 crashes in both periods.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in adverse weather conditions became more prominent in March 2022, with 4 crashes in 'Snow' conditions and 3 in 'Cloudy/Rain' conditions, which were not present in March 2021. Similarly, crashes on 'Wet' road surfaces increased from 2 to 7, and 'Ice' and 'Snow' road conditions appeared with 5 and 3 crashes respectively in the current period. While 'Clear' weather and 'Dry' road surfaces still accounted for the majority of crashes, their proportional share decreased as incidents under adverse conditions rose.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 67 to 102 year-over-year. Toyota remained the top vehicle make involved, increasing from 12 to 17 vehicles, while Honda saw a significant rise from 6 to 12 vehicles. All age groups experienced an increase in persons involved in crashes, with the 16-20 age group showing a notable rise from 4 to 13 persons.
Top Vehicle Makes (102 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Vehicle unit records
7 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 · 2022-03-01 to 2022-03-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in the 35 mph speed zone saw the largest increase, rising from 10 incidents in March 2021 to 26 in March 2022. Crashes in the 30 mph zone also increased from 7 to 13, and in the 65 mph zone from 3 to 5. No fatal crashes were recorded in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · 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: 2022-03-01 through 2022-03-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-03-01 through 2022-03-31 (31 days)
- Geographic scope: TEWKSBURY, MA
- Total crash records analyzed: 57
- Total persons involved: 114
- Total vehicles involved: 102
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). "TEWKSBURY, MA Crash Intelligence Report: March 2022." Published June 21, 2026. Reporting period: 2022-03-01 to 2022-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/tewksbury/march-2022-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: 2022-03-01 – 2022-03-31
Generated: June 21, 2026 · All rights reserved