ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PALMER, 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/palmer/2023-annual-report
Yearly Traffic Safety Analysis
362 CRASHES IN
PALMER, MA
2023
In 2023, Palmer recorded 362 total crashes, a 10% increase from the 329 crashes in 2022. The most significant year-over-year change was the increase in total fatalities from zero in 2022 to three in 2023. The number of people injured also rose sharply, from 66 to 127 during the same period.
362
▲ 10.0%was 329
Total Crash Events
3
Persons Killed
127
▲ 92.4%was 66
Persons Injured
27
▲ 35.0%was 20
Hit-and-Run Crashes
Note: "Persons Killed" (3) 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. 13 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
Crash trends in Palmer show a clear increase from 2022 to 2023. Total crashes rose by 10% from 329 to 362. More concerningly, the number of people injured increased by 92%, from 66 to 127, and fatalities rose from zero to three.
27
Hit-and-Run Crashes — 2023
▲ 35.0% vs prior (20)
Hit-and-run incidents trended upward from 2022 to 2023. The total number of hit-and-run crashes increased from 20 to 27. As a percentage of all crashes, the hit-and-run rate also rose from 6.1% in the prior period to 7.5% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
3
Motorists Killed
0
Other Killed
4
Pedestrians Injured
1
Cyclists Injured
121
Motorists Injured
1
Other 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 temporal patterns of crashes shifted between the two periods. The peak day for crashes moved from Friday (55 crashes) in 2022 to Thursday (59 crashes) in 2023. The peak hour also shifted two hours earlier, from 4 p.m. in 2022 (29 crashes) to 2 p.m. in 2023, which saw a higher concentration of 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
Crash severity worsened significantly in 2023 compared to 2022. The city recorded three fatal crashes in 2023 after having none in the prior year, raising the fatal crash rate from 0% to 0.83%. The proportion of crashes resulting in a serious injury more than doubled, increasing from 1.2% to 3.6% of all incidents. Correspondingly, the share of non-injury crashes decreased from 79% in 2022 to 67.7% in 2023.
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
A comparison of contributing factors reveals a significant shift in collision dynamics. Crashes attributed to 'Failed to yield right of way' increased dramatically in count from 10 in 2022 to 44 in 2023, a 340% rise that elevated it from the seventh to the second most common factor. In contrast, crashes involving 'Inattention' as a primary factor decreased in count by 22%, from 54 to 42 incidents. The count for 'Followed too closely' also rose from 20 to 25 crashes.
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
While the majority of crashes in both periods occurred in clear weather on dry roads, there was an increase in crashes under adverse conditions. The number of crashes on wet roads rose from 43 in 2022 to 62 in 2023, and crashes in the rain increased from 14 to 26. The proportion of crashes occurring during daylight remained stable at 69% for both years.
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 were Ford, Toyota, and Chevrolet in both years, though their ranking shifted, with Ford becoming the most frequent make in 2023 (71 vehicles) up from third in 2022 (54 vehicles). Demographically, the number of individuals involved in crashes increased from 652 to 761. The proportion of involved persons aged 65 and older remained steady at approximately 14% in both years.
Top Vehicle Makes (598 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
66 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (698 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
In 2023, three fatal crashes occurred in speed zones of 20 mph, 35 mph, and 40 mph; no fatal crashes were recorded in any speed zone in 2022. The distribution of crashes shifted slightly, with a notable increase in incidents within 30 mph zones, which rose from 85 to 104. Conversely, crashes in 65 mph zones saw a slight decrease from 82 to 78.
Fatal crashes by zone: 20 mph: 1 of 7 (14.286%) · 35 mph: 1 of 52 (1.923%) · 40 mph: 1 of 53 (1.887%)
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: PALMER, MA
- Total crash records analyzed: 362
- Total persons involved: 761
- Total vehicles involved: 598
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: 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/palmer/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