ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · GARDNER, 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/gardner/2022-annual-report
Yearly Traffic Safety Analysis
544 CRASHES IN
GARDNER, MA
2022
In Gardner, total traffic crashes increased by 5.2%, from 517 incidents in 2021 to 544 in 2022. While the total number of injuries remained unchanged at 98, the most significant year-over-year shift was a substantial increase in fatalities, which rose from 1 in 2021 to 6 in 2022. The number of hit-and-run crashes also saw a notable increase during this period.
544
▲ 5.2%was 517
Total Crash Events
6
▲ 500.0%was 1
Persons Killed
98
Persons Injured
35
▲ 191.7%was 12
Hit-and-Run Crashes
Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 25 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 in traffic incidents shows an increase year-over-year. Total crashes rose from 517 in 2021 to 544 in 2022, a 5.2% increase. This increase was marked by a significant rise in crash severity, with total fatalities climbing from 1 to 6, while the number of persons injured remained stable at 98 for both years.
35
Hit-and-Run Crashes — 2022
▲ 191.7% vs prior (12)
Hit-and-run incidents increased substantially between 2021 and 2022. The total count of hit-and-run crashes rose from 12 to 35, a 191.7% increase. As a result, the hit-and-run rate, which measures the proportion of all crashes that are hit-and-runs, climbed from 2.3% in 2021 to 6.4% in 2022, indicating a rising trend.
Vulnerable Road User Casualties
2
Pedestrians Killed
0
Cyclists Killed
4
Motorists Killed
4
Pedestrians Injured
4
Cyclists Injured
90
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 remained largely consistent between the two periods. Tuesday was the peak day for crashes in both 2021 and 2022, with an identical count of 91 incidents each year. The peak hour for collisions saw a minor shift, moving from 3 p.m. in 2021 (47 crashes) to 2 p.m. in 2022 (48 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 worsened significantly from 2021 to 2022. The number of fatal crashes increased from 1 to 6, causing the fatal crash rate to climb from 0.19% to 1.1% of all crashes. In contrast, crashes involving serious injuries decreased, falling from 6 incidents (1.2% of total) in 2021 to 3 incidents (0.6% of total) in 2022. The proportion of crashes resulting in no injuries grew from 77.0% to 80.5%.
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 to crashes remained consistent, with 'Inattention' and 'No improper driving' ranking as the top two in both years. The count of crashes attributed to 'Inattention' increased by 42.2%, from 109 in 2021 to 155 in 2022. Conversely, crashes involving an 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' decreased in count from 45 to 24, a 46.7% drop.
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 environmental conditions showed some shifts between 2021 and 2022. The proportion of crashes occurring in daylight increased from 68.1% (352 crashes) to 75.2% (409 crashes), while those in dark conditions decreased from 28.0% (145 crashes) to 19.9% (108 crashes). The share of crashes on dry road surfaces remained stable at approximately 76.5%, and the proportion of crashes in clear weather was also consistent, accounting for 70.0% of crashes in 2021 and 73.3% in 2022.
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 makes of vehicles involved in crashes were similar year-over-year, with Toyota, Chevrolet, and Ford being the top three in both periods. The number of Subarus involved in crashes saw a notable increase, rising from 59 in 2021 to 88 in 2022. The age distribution of persons involved in crashes also remained relatively stable, with the 26-34 age group being the largest in both years (179 persons in 2021 and 174 in 2022).
Top Vehicle Makes (999 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
179 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,003 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 30 mph speed zone continued to be the location with the highest number of crashes, increasing from 217 incidents in 2021 to 229 in 2022. In 2022, this zone also recorded 3 fatal crashes, whereas it had none in the prior year. The 45 mph zone accounted for one fatal crash in 2021 and one in 2022. Crashes in 20 mph zones increased from 82 to 111 year-over-year.
Fatal crashes by zone: 30 mph: 3 of 229 (1.31%) · 45 mph: 1 of 13 (7.692%) · 50 mph: 1 of 10 (10%)
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: GARDNER, MA
- Total crash records analyzed: 544
- Total persons involved: 1,184
- Total vehicles involved: 999
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). "GARDNER, 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/gardner/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