ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · SHARON, MA · MAY 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/sharon/may-2024-report
Monthly Traffic Safety Analysis
32 CRASHES IN
SHARON, MA
MAY 2024
The city of SHARON experienced a stable number of total crashes in May 2024, with 32 incidents, matching the 32 crashes reported in May 2023. A significant year-over-year shift was observed in total injuries, which decreased by 57.1% from 14 injuries in May 2023 to 6 injuries in May 2024. Additionally, crashes involving speeding dropped to zero in May 2024, down from 3 incidents in the prior year.
32
Total Crash Events
0
Persons Killed
6
▼ -57.1%was 14
Persons Injured
0
▼ -100.0%was 2
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.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, the total number of crashes in SHARON remained stable year-over-year, with 32 crashes recorded in both May 2024 and May 2023. Despite this stability in crash count, there was a notable positive trend in injury reduction, as total injuries decreased by 57.1% from 14 to 6. Fatalities remained at zero for both periods, indicating no change in the most severe outcomes.
Vulnerable Road User Casualties
0
Motorists Killed
6
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns for crashes shifted between the two periods. In May 2024, Friday was the peak day for crashes with 8 incidents, whereas Wednesday held the peak in May 2023, also with 8 incidents. The peak hour for crashes also changed, moving from 3 PM with 6 crashes in May 2023 to 8 AM with 6 crashes in May 2024.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatalities remained at 0 in both May 2024 and May 2023. Total injuries saw a substantial decrease, falling from 14 in May 2023 to 6 in May 2024. This reduction was primarily driven by a decrease in minor injuries (severity B), which dropped from 7 incidents (21.9% of crashes) to 1 incident (3.1% of crashes), while possible injuries (severity C) increased from 2 incidents (6.3% of crashes) to 4 incidents (12.5% of crashes).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Most severe injury per crash record
Top Contributing Factors
The top contributing factors showed shifts in their prevalence and ranking. 'Followed too closely' increased from 7 crashes in May 2023 to 9 crashes in May 2024, becoming the leading factor. Conversely, 'Inattention' decreased from 7 crashes to 5 crashes, dropping in rank, and 'Driving too fast for conditions' was eliminated as a factor, falling from 3 crashes to 0. 'Failed to yield right of way' saw a slight increase from 5 crashes to 6 crashes.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in rainy weather conditions increased from 2 incidents in May 2023 to 5 incidents in May 2024, while clear weather crashes decreased from 27 to 23. Regarding lighting, crashes during daylight hours decreased from 27 to 22, and incidents in dark conditions with unlighted roadways increased from 2 to 5. On road surfaces, wet road crashes doubled from 3 to 6, while dry road crashes slightly decreased from 28 to 26.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Road surface condition field
Vehicles & Demographics
The composition of vehicle makes involved in crashes saw some changes, with HONDA becoming the most frequent make in May 2024, increasing from 8 to 11 incidents, while TOYOTA increased from 8 to 9. The age distribution of persons involved in crashes also shifted; the 16-20 age group saw an increase from 8 to 12 persons, and the 65+ age group increased from 6 to 11 persons. Conversely, the 0-15 age group experienced a decrease from 7 to 4 persons involved.
Top Vehicle Makes (62 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Vehicle unit records
1 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (77 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Person-level records linked to crash events
Speed Limit Zones
Crash distribution across speed zones showed changes, with incidents in the 35 mph zone increasing from 7 to 12. Crashes in the 25 mph zone decreased significantly from 7 to 1, and those in the 65 mph zone decreased from 8 to 6. Additionally, crashes were reported in new speed limit categories for May 2024, including 5 mph, 10 mph, and 50 mph zones, each with 1 incident.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · 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: 2024-05-01 through 2024-05-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-05-01 through 2024-05-31 (31 days)
- Geographic scope: SHARON, MA
- Total crash records analyzed: 32
- Total persons involved: 79
- Total vehicles involved: 62
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). "SHARON, MA Crash Intelligence Report: May 2024." Published June 21, 2026. Reporting period: 2024-05-01 to 2024-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/sharon/may-2024-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: 2024-05-01 – 2024-05-31
Generated: June 21, 2026 · All rights reserved