ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BELCHERTOWN, 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/belchertown/2024-annual-report
Yearly Traffic Safety Analysis
211 CRASHES IN
BELCHERTOWN, MA
2024
In Belchertown, total crashes increased by 7.7% from 196 incidents in 2023 to 211 in 2024. While the number of fatalities decreased from two to one and total injuries fell from 53 to 43, the most notable year-over-year change was the doubling of hit-and-run crashes, which rose from 4 to 8 incidents.
211
▲ 7.7%was 196
Total Crash Events
1
▼ -50.0%was 2
Persons Killed
43
▼ -18.9%was 53
Persons Injured
8
▲ 100.0%was 4
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. 4 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, traffic crashes in Belchertown trended upward, with a 7.7% increase from 196 incidents in 2023 to 211 in 2024. Despite the rise in total collisions, the number of resulting injuries fell by 18.9% from 53 to 43, and fatalities were halved from two to one.
8
Hit-and-Run Crashes — 2024
▲ 100.0% vs prior (4)
Hit-and-run incidents showed a significant upward trend. The number of hit-and-run crashes doubled, increasing from 4 in 2023 to 8 in 2024. Consequently, the hit-and-run rate, which measures the percentage of all crashes that are hit-and-runs, increased from 2.0% to 3.8%.
Vulnerable Road User Casualties
1
Motorists Killed
43
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 shifted between the two periods. In 2024, the peak day for crashes was Tuesday with 44 incidents, a change from the prior year's peak on Friday with 38 incidents. Similarly, the peak hour for crashes moved earlier in the day, from 5 PM in 2023 (18 crashes) to 2 PM in 2024 (22 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
The severity of crashes decreased from 2023 to 2024. The fatal crash rate was more than halved, dropping from 1.02 to 0.47, with fatal incidents accounting for 0.5% of all crashes compared to 1.0% in the prior year. The proportion of crashes resulting in any level of injury decreased from 23.4% in 2023 to 16.6% in 2024, while property-damage-only crashes increased from a 74.0% share to 81.5% of the total.
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 top contributing factors showed some shifts between periods. While 'No improper driving' remained the most common finding in both years, its count increased from 74 to 89. The count for 'Inattention' decreased from 28 to 21, though it remained the second-most cited improper driving factor. Crashes attributed to 'Failed to yield right of way' decreased from 10 to 6, while those involving 'Failure to keep in proper lane' also saw a decrease from 10 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 environmental conditions under which crashes occurred remained largely consistent year-over-year. The proportion of crashes happening in daylight (65.0% vs 66.3% prior) and on dry roads (72.0% vs 75.0% prior) saw minimal change. However, there was a shift in adverse road surface conditions, with crashes on wet surfaces decreasing from 36 to 25, while crashes on snowy surfaces increased from 10 to 22.
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 minor shuffle, with Ford (42 vehicles) overtaking Toyota (40 vehicles) for the top spot in 2024, reversing the prior year's ranking. More significant changes were observed in the age demographics of persons involved in crashes. The number of persons in the 21-25 age group more than doubled from 28 to 57, while the 0-15 age group saw a sharp decrease from 112 to 25 persons involved.
Top Vehicle Makes (309 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
20 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (368 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
The distribution of crashes across speed zones shifted, with the 30 mph zone seeing the most incidents in 2024 (67 crashes), up from 51 in the prior year. This surpassed the 40 mph zone, which had the highest count in 2023 with 52 crashes and increased to 57 crashes in 2024. The single fatal crash in 2024 occurred in a 30 mph zone; in the previous year, one fatality occurred in a 30 mph zone and another in a 40 mph zone.
Fatal crashes by zone: 30 mph: 1 of 67 (1.493%)
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: BELCHERTOWN, MA
- Total crash records analyzed: 211
- Total persons involved: 387
- Total vehicles involved: 309
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). "BELCHERTOWN, 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/belchertown/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