ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PAXTON, 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/paxton/2024-annual-report
Yearly Traffic Safety Analysis
58 CRASHES IN
PAXTON, MA
2024
In Paxton, the total number of crashes remained unchanged year-over-year, with 58 incidents recorded in both 2024 and 2023. While the overall crash volume was stable, the most significant development was the occurrence of one fatal crash in 2024, resulting in one fatality, compared to zero fatal crashes in the prior year. The total number of people injured increased slightly from 19 in 2023 to 20 in 2024.
58
Total Crash Events
1
Persons Killed
20
▲ 5.3%was 19
Persons Injured
2
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. 1 crash with unreported severity is 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 crash trends in Paxton remained stable between 2023 and 2024, with the total number of crashes holding steady at 58. However, the severity of these incidents worsened, marked by the appearance of one fatality in 2024 after none were recorded in the previous year. The total number of injuries also increased slightly from 19 to 20.
2
Hit-and-Run Crashes — 2024
▼ 0.0% vs prior (2)
The number and rate of hit-and-run crashes in Paxton remained unchanged year-over-year. Both 2023 and 2024 recorded 2 hit-and-run incidents. As the total number of crashes was also stable, the hit-and-run rate held steady at 3.4% of all crashes.
Vulnerable Road User Casualties
1
Motorists Killed
20
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 timing of crashes in Paxton shifted between the two periods. The most frequent day for crashes moved from Monday (12 crashes) in 2023 to Thursday (13 crashes) in 2024. Similarly, the peak hour for collisions shifted an hour later, from 5 PM in 2023 (8 crashes) to 6 PM in 2024 (7 crashes).
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
Crash severity saw a mixed change year-over-year, with the introduction of a fatal crash in 2024 (1.7% of all crashes), whereas there were none in 2023. Despite this, the overall proportion of crashes resulting in any level of injury decreased, falling from 27.6% (16 crashes) in 2023 to 20.6% (12 crashes) in 2024. The number of serious injury crashes also declined from 3 to 1.
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 for crashes shifted between 2023 and 2024. The most notable increase was in crashes attributed to 'Driving too fast for conditions,' which rose from 1 incident in 2023 to 6 in 2024. Conversely, crashes involving 'Failure to keep in proper lane or running off road' decreased from 5 to 1. 'Inattention' remained a top factor, increasing from 6 to 8 incidents.
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
The conditions under which crashes occurred varied year-over-year. Crashes on roads with snow present increased substantially from 1 incident in 2023 to 12 in 2024, and crashes during snowy weather rose from 2 to 8. In contrast, crashes on wet roads decreased from 18 to 6. Regarding lighting, crashes during daylight hours rose from 29 to 34, while incidents on dark but lighted roadways increased from 12 to 17.
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 profile of vehicles and persons involved in crashes showed some shifts. Toyota was the most common vehicle make in both 2023 (12 vehicles) and 2024 (13 vehicles), while Ford's involvement increased from 8 to 10 vehicles. Regarding persons involved, the largest age cohort shifted from the 35-44 group in 2023 (21 persons) to a three-way tie between the 16-20, 26-34, and 35-44 age groups in 2024 (16 persons each).
Top Vehicle Makes (92 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
7 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (101 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
Crashes became more concentrated in higher speed zones in 2024 compared to the previous year. The number of crashes in 40 mph zones increased from 26 in 2023 to 31 in 2024, while incidents in 30 mph zones remained constant at 21. The single fatal crash recorded in 2024 occurred in a 40 mph zone; no fatal crashes were recorded in 2023.
Fatal crashes by zone: 40 mph: 1 of 31 (3.226%)
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: PAXTON, MA
- Total crash records analyzed: 58
- Total persons involved: 108
- Total vehicles involved: 92
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). "PAXTON, 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/paxton/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