ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PALMER, MA · 2024
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/2024-annual-report
Yearly Traffic Safety Analysis
400 CRASHES IN
PALMER, MA
2024
In Palmer, total traffic crashes increased by 10.5% from 362 in 2023 to 400 in 2024. Despite this increase in collision volume, the number of fatalities recorded decreased from three to one year-over-year. The most notable shift was a 65.2% increase in the count of crashes where speeding was cited as a contributing factor, rising from 23 to 38 incidents.
400
▲ 10.5%was 362
Total Crash Events
1
▼ -66.7%was 3
Persons Killed
126
▼ -0.8%was 127
Persons Injured
23
▼ -14.8%was 27
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 10 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, Palmer experienced a rising trend in the number of crashes, which increased from 362 in the prior year to 400 in the current year. This represents a 10.5% year-over-year increase in total collisions. However, the severity of these incidents lessened, with total fatalities dropping from 3 to 1 and total injuries remaining stable, decreasing from 127 to 126.
23
Hit-and-Run Crashes — 2024
▼ -14.8% vs prior (27)
Hit-and-run incidents trended downward in the current period compared to the prior year. The total count of hit-and-run crashes decreased from 27 to 23. Consequently, the hit-and-run rate, as a percentage of total crashes, also fell from 7.5% in 2023 to 5.8% in 2024.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
3
Pedestrians Injured
1
Cyclists Injured
122
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes remained broadly consistent year-over-year. Thursday was the peak day for crashes in both 2024 (74 crashes) and 2023 (59 crashes). The peak hour for collisions shifted slightly, moving from the 2 PM hour in 2023 (39 crashes) to the 3 PM hour in 2024 (38 crashes), indicating a consistent concentration of incidents during the afternoon.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes decreased compared to the prior year. The fatal crash rate fell from 0.83% to 0.25%, with fatal crashes dropping from 3 to 1. The proportion of crashes resulting in serious injuries also decreased from 3.6% to 1.5% of all incidents. Correspondingly, the share of non-injury crashes increased from 67.7% in 2023 to 72.3% in 2024.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors remained consistent between the two periods, with 'No improper driving', 'Inattention', and 'Failed to yield right of way' being the top three in both years. The count of crashes attributed to 'Inattention' increased by 28.6%, from 42 to 54 incidents. Most notably, the combined count for speeding-related factors ('Driving too fast for conditions' and 'Exceeded authorized speed limit') rose from 23 to 38, a 65.2% increase in count.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in clear weather and on dry roads remained the most common scenario in both years. However, there was a significant increase in crashes occurring during adverse winter conditions. Crashes in snow weather conditions increased from 7 to 26, and incidents on snow-covered road surfaces rose from 10 to 40. The proportion of crashes in daylight decreased slightly from 69.1% to 66.0%.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes saw a slight shift in ranking, with Toyota (92 vehicles) overtaking Ford (74 vehicles) for the top spot compared to the prior year where Ford led. An analysis of persons involved shows a notable increase in the 26-34 age group (from 120 to 155 persons) and the 55-64 age group (from 78 to 104 persons). Conversely, the number of persons in the 65+ age group involved in crashes decreased from 108 to 94.
Top Vehicle Makes (700 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
78 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (764 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
There was a noticeable shift in where crashes occurred, with incidents in 30 mph zones increasing from 104 to 146 year-over-year. In contrast, crashes in 40 mph zones decreased from 53 to 29. The single fatal crash in 2024 occurred in a 30 mph zone, whereas the three fatalities in 2023 were distributed across 20, 35, and 40 mph zones.
Fatal crashes by zone: 30 mph: 1 of 146 (0.685%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: PALMER, MA
- Total crash records analyzed: 400
- Total persons involved: 847
- Total vehicles involved: 700
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: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/palmer/2024-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: 2024-01-01 – 2024-12-31
Generated: June 21, 2026 · All rights reserved