ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PALMER, MA · JANUARY 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/palmer/january-2023-report
Monthly Traffic Safety Analysis
36 CRASHES IN
PALMER, MA
JANUARY 2023
In January 2023, Palmer experienced 36 crashes, a 2.9% increase from the 35 crashes recorded in January 2022. The total number of injuries rose by 60%, from 5 to 8, despite the small increase in overall crash count. The most significant year-over-year shift was a 100% increase in hit-and-run crashes, rising from 1 to 2 incidents.
36
▲ 2.9%was 35
Total Crash Events
0
Persons Killed
8
▲ 60.0%was 5
Persons Injured
2
▲ 100.0%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. 3 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 2023-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash incidents in Palmer saw a slight increase year-over-year, rising from 35 crashes in January 2022 to 36 crashes in January 2023, representing a 2.9% increase. Total injuries increased by 60%, from 5 to 8, indicating a worsening outcome despite the small rise in total crash count.
2
Hit-and-Run Crashes — January 2023
▲ 100.0% vs prior (1)
Hit-and-run crashes increased by 100% year-over-year, rising from 1 incident in January 2022 to 2 incidents in January 2023. Consequently, the hit-and-run rate also increased from 2.9% to 5.6% of all crashes. This indicates an upward trend in hit-and-run incidents.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
1
Pedestrians Injured
7
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak day for crashes shifted from Monday in January 2022, with 10 crashes, to Tuesday in January 2023, also with 10 crashes. The peak hour also changed, moving from 8 AM with 6 crashes in the prior period to 2 PM with 7 crashes in the current period. This indicates a shift in the busiest times for crash occurrences.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatalities remained at zero in both January 2022 and January 2023. However, total injuries increased by 60%, from 5 in the prior period to 8 in the current period. The proportion of crashes involving injuries rose from 14.3% in January 2022 to 22.2% in January 2023, with the current period also recording 1 serious injury crash where none were reported previously.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Most severe injury per crash record
Top Contributing Factors
The top contributing factor, 'No improper driving,' decreased from 9 crashes in January 2022 to 8 crashes in January 2023. Conversely, 'Inattention' increased from 3 crashes to 5 crashes, and 'Failed to yield right of way' rose from 1 to 3 crashes. 'Driving too fast for conditions' also saw an increase from 2 to 3 crashes year-over-year.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes on dry road surfaces decreased from 17 in January 2022 to 15 in January 2023, while crashes on wet surfaces increased significantly from 4 to 16 during the same period. Regarding weather, 'Clear' conditions saw a decrease in associated crashes from 16 to 12. In terms of lighting, crashes occurring in 'Dark - lighted roadway' conditions increased from 7 to 10.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 53 in January 2022 to 60 in January 2023. In the prior period, CHEVROLET was the most frequent vehicle make involved in crashes with 11 incidents, while in the current period, TOYOTA, HONDA, and NISSAN shared the top spot with 6 incidents each. The age group 65+ saw an increase in persons involved from 8 to 13, and the 16-20 age group increased from 4 to 7 persons involved.
Top Vehicle Makes (60 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Vehicle unit records
5 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (64 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 30 mph zones decreased from 16 in January 2022 to 10 in January 2023. Conversely, crashes in 40 mph zones increased from 2 to 8, and those in 65 mph zones doubled from 2 to 6. There were no fatal crashes reported in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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 2023-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-01-31 (31 days)
- Geographic scope: PALMER, MA
- Total crash records analyzed: 36
- Total persons involved: 71
- Total vehicles involved: 60
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). "PALMER, MA Crash Intelligence Report: January 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/palmer/january-2023-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 – 2023-01-31
Generated: June 21, 2026 · All rights reserved