ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · EAST LONGMEADOW, MA · 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.
Yearly Traffic Safety Analysis
303 CRASHES IN
EAST LONGMEADOW, MA
2025
In 2025, East Longmeadow recorded 303 total vehicle crashes, a 2.9% decrease from the 312 crashes reported in 2024. While overall crash volume remained relatively stable, there was a notable 45.8% decrease in hit-and-run incidents. Conversely, crashes attributed to inattention as a contributing factor increased by 43.9% year-over-year.
303
▼ -2.9%was 312
Total Crash Events
0
Persons Killed
94
▼ -6.0%was 100
Persons Injured
13
▼ -45.8%was 24
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. 3 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, traffic crashes in East Longmeadow showed a slight downward trend year-over-year. The total number of crashes decreased by 2.9%, from 312 in 2024 to 303 in 2025. Similarly, the total number of injuries fell by 6.0%, from 100 to 94.
13
Hit-and-Run Crashes — 2025
▼ -45.8% vs prior (24)
Hit-and-run incidents showed a significant downward trend in 2025 compared to the previous year. The total number of hit-and-run crashes decreased by 45.8%, from 24 in 2024 to 13 in 2025. This drop was also reflected in the hit-and-run rate, which fell from 7.7% of all crashes in 2024 to 4.3% in 2025.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
2
Pedestrians Injured
3
Cyclists Injured
89
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 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 patterns of crashes saw some shifts between the two years. The peak day for crashes moved from Friday (56 incidents) in 2024 to Tuesday (59 incidents) in 2025. The peak hour for collisions also shifted slightly later, from the 4 p.m. hour in 2024 (29 crashes) to the 5 p.m. hour in 2025 (30 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity remained consistent at the highest level, with zero fatalities recorded in both 2024 and 2025. The proportion of crashes resulting in serious injury was stable, at 2.2% in 2024 and 2.6% in 2025. However, there was a shift in non-serious outcomes, with the share of minor injury crashes decreasing from 16.0% to 12.2% as the share of no-injury crashes rose from 73.7% to 77.9%.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor to crashes in both periods was 'Failed to yield right of way,' with the count increasing by 7.1% from 84 incidents in 2024 to 90 in 2025. A significant shift occurred with 'Inattention,' which saw its count jump by 43.9% from 41 to 59 crashes, moving it from the third to the second most common factor. Conversely, crashes attributed to 'Followed too closely' decreased in count by 32.4% (from 34 to 23).
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The majority of crashes in both years occurred during daylight on dry roads, with the proportion of daylight crashes remaining stable at around 71%. There was a marked decrease in crashes occurring on adverse road surfaces; the count of crashes on wet roads fell from 52 to 34, and incidents on snow, ice, or slush dropped from 28 to 10. Consequently, the share of crashes on dry roads increased from 74.4% in 2024 to 85.1% in 2025.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field
Vehicles & Demographics
Toyota was the most common vehicle make involved in crashes during both periods, followed by Honda and Ford. In 2025, Ford (64 vehicles) surpassed Honda (61 vehicles) for the second position, a reversal from 2024 when Honda (75) ranked ahead of Ford (48). Analysis of persons involved shows a demographic shift, with the share of individuals aged 65 and older increasing from 15.0% of total persons in 2024 to 19.3% in 2025.
Top Vehicle Makes (557 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
39 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (664 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes were most prevalent in 25 mph and 35 mph speed zones in both years, with no fatal crashes recorded in any zone during either period. The 35 mph zone saw a decrease in crashes from 134 in 2024 to 113 in 2025, while the 25 mph zone experienced a slight increase from 99 to 107 incidents. Overall, the distribution of crashes across different speed zones remained largely consistent year-over-year.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 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: 2025-01-01 through 2025-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-01-01 through 2025-12-31 (365 days)
- Geographic scope: EAST LONGMEADOW, MA
- Total crash records analyzed: 303
- Total persons involved: 710
- Total vehicles involved: 557
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). "EAST LONGMEADOW, MA Crash Intelligence Report: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/east-longmeadow/2025-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: 2025-01-01 – 2025-12-31
Generated: June 21, 2026 · All rights reserved