ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · GARDNER, MA · 2023
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/2023-annual-report
Yearly Traffic Safety Analysis
434 CRASHES IN
GARDNER, MA
2023
In Gardner, traffic crashes decreased by 20.2% year-over-year, falling from 544 incidents in 2022 to 434 in 2023. This overall reduction in crash volume represents the most significant trend in the data. While total crashes and fatalities declined, the number of reported injuries saw a slight increase over the same period.
434
▼ -20.2%was 544
Total Crash Events
4
▼ -33.3%was 6
Persons Killed
106
▲ 8.2%was 98
Persons Injured
32
▼ -8.6%was 35
Hit-and-Run Crashes
Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 30 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Traffic safety trends in Gardner showed a notable improvement in the total volume of crashes, which fell by 20.2% from 544 in the prior year to 434 in the current year. Fatalities also decreased from 6 to 4. However, this positive trend did not extend to injuries, which rose by 8.2% from 98 to 106.
32
Hit-and-Run Crashes — 2023
▼ -8.6% vs prior (35)
The total number of hit-and-run crashes saw a slight decrease from 35 to 32 incidents year-over-year. However, because total crashes fell more significantly, the hit-and-run rate as a percentage of all crashes trended upward, increasing from 6.4% in the prior period to 7.4% in the current period.
Vulnerable Road User Casualties
1
Pedestrians Killed
1
Cyclists Killed
2
Motorists Killed
5
Pedestrians Injured
6
Cyclists Injured
95
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of crashes shifted between the two periods. The most common day for a crash moved from Tuesday (91 crashes) in the prior year to Friday (83 crashes) in the current year. Similarly, the peak hour for incidents shifted slightly later in the day, from 2 PM (48 crashes) to 3 PM (39 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While the total number of fatal crashes halved from 6 to 3 year-over-year, the overall severity of non-fatal crashes increased. The proportion of crashes resulting in some form of injury (fatal, serious, minor, or possible) rose from 14.9% in the prior period to 18.2% in the current period. This was driven by an increase in the number of serious injury crashes (from 3 to 5) and minor injury crashes (from 46 to 54).
Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
Inattention remained the leading contributing factor in both years, though its count fell by 13.5% from 155 to 134 crashes. The top three factors were consistent across both periods, but their counts shifted. Notably, crashes attributed to 'Failed to yield right of way' increased in count by 12.5%, rising from 48 to 54 incidents, and its share of total crashes grew from 8.8% to 12.4%.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The conditions under which crashes occurred remained broadly similar year-over-year. Crashes on non-dry road surfaces (wet, snow, ice, or slush) accounted for a slightly smaller proportion of incidents, decreasing from 22.8% of all crashes in the prior year to 20.7% in the current year. Conversely, the share of crashes occurring in dark or low-light conditions saw a slight increase, from 23.2% to 24.4% of the total.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes—Toyota, Ford, and Chevrolet—were identical in both years, though the total number of vehicles from each make decreased in line with the overall crash reduction. An analysis of persons involved shows a demographic shift, with the proportion of individuals aged 65 and older decreasing from 12.3% of all persons in the prior year to 10.2% in the current year.
Top Vehicle Makes (774 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
143 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (826 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes became more concentrated in lower-speed areas year-over-year. The proportion of incidents occurring in 30 mph zones increased from 42.4% to 49.5% of all crashes with a recorded speed limit. In the prior year, 3 of the 6 fatal crashes occurred in 30 mph zones, whereas in the current year, the 3 fatal crashes were distributed across 30 mph, 40 mph, and 45 mph zones.
Fatal crashes by zone: 30 mph: 1 of 201 (0.498%) · 40 mph: 1 of 17 (5.882%) · 45 mph: 1 of 10 (10%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: GARDNER, MA
- Total crash records analyzed: 434
- Total persons involved: 972
- Total vehicles involved: 774
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: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/gardner/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved