ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · LEICESTER, MA · DECEMBER 2025
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/leicester/december-2025-report
Monthly Traffic Safety Analysis
29 CRASHES IN
LEICESTER, MA
DECEMBER 2025
In December 2025, LEICESTER experienced 29 crashes, a substantial increase compared to the 13 crashes reported in December 2024, representing a 123.08% rise. This period also saw an increase in total injuries, rising from 2 to 5 year-over-year.
29
▲ 123.1%was 13
Total Crash Events
0
Persons Killed
5
▲ 150.0%was 2
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. 1 crash with unreported severity is not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash activity in LEICESTER shows an upward trend year-over-year, with total crashes increasing by 123.08%, from 13 in December 2024 to 29 in December 2025. Concurrently, total injuries rose by 150%, from 2 to 5, indicating a general increase in crash-related harm.
2
Hit-and-Run Crashes — December 2025
▲ 100.0% vs prior (1)
The number of hit-and-run crashes increased from 1 in December 2024 to 2 in December 2025. Despite this increase in count, the hit-and-run rate decreased slightly from 7.7% of total crashes in the prior period to 6.9% in the current period.
Vulnerable Road User Casualties
0
Motorists Killed
5
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal distribution of crashes shifted year-over-year, with the peak day moving from Thursday with 8 crashes in December 2024 to Saturday with 6 crashes in December 2025. The peak crash hour also changed, from 11p with 1 crash in December 2024 to 5p with 3 crashes in December 2025. Additionally, December 2025 saw a broader distribution of crashes across weekdays, including 6 crashes on both Monday and Tuesday, whereas December 2024 had no crashes on Monday, Wednesday, Friday, or Saturday.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Both December 2024 and December 2025 reported zero fatalities and fatal crashes. Total injuries increased from 2 in the prior period to 5 in the current period, representing a 150% rise. Minor injury crashes saw a notable increase, rising from 1 (7.7% of crashes) in December 2024 to 5 (17.2% of crashes) in December 2025, while possible injury crashes, present in December 2024, were not reported in December 2025.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor, 'No improper driving', increased from 7 crashes in December 2024 to 10 crashes in December 2025. 'Inattention' also saw a rise in count, from 3 crashes in the prior period to 4 crashes in the current period. Notably, factors such as 'Disregarded traffic signs, signals, road markings' and 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' emerged in December 2025, each contributing to 3 crashes, while 'Followed too closely' was reported in December 2024 but not in December 2025.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in clear weather conditions increased significantly from 6 in December 2024 to 24 in December 2025, while those in snowy conditions decreased from 6 to 2. Similarly, crashes on dry road surfaces rose from 6 to 20, though crashes on snowy roads remained stable at 5. Crashes occurring during daylight hours also increased, from 5 in December 2024 to 18 in December 2025.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Road surface condition field
Vehicles & Demographics
Top Vehicle Makes (48 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Vehicle unit records
6 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (55 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 30 mph zones increased from 6 in December 2024 to 11 in December 2025, while those in 35 mph zones rose from 5 to 7. Crashes in 25 mph zones remained stable at 2 for both periods. The current period also reported crashes in a broader range of speed zones, including 5, 10, 20, 40, and 45 mph, which were not present in the prior period's data.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-12-01 to 2025-12-31 · 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: 2025-12-01 through 2025-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-12-01 through 2025-12-31 (31 days)
- Geographic scope: LEICESTER, MA
- Total crash records analyzed: 29
- Total persons involved: 61
- Total vehicles involved: 48
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). "LEICESTER, MA Crash Intelligence Report: December 2025." Published June 21, 2026. Reporting period: 2025-12-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/leicester/december-2025-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: 2025-12-01 – 2025-12-31
Generated: June 21, 2026 · All rights reserved