ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CHICOPEE, 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.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/chicopee/2025-annual-report
Yearly Traffic Safety Analysis
1,604 CRASHES IN
CHICOPEE, MA
2025
In 2025, Chicopee recorded 1,604 total traffic crashes, a 2.3% decrease from the 1,641 crashes documented in 2024. While total crashes and injuries (472, down from 500) declined, fatalities remained constant at 5 deaths in both periods. The most notable year-over-year change was a 22.3% increase in the count of crashes attributed to inattention, which rose from 215 to 263 incidents.
1,604
▼ -2.3%was 1,641
Total Crash Events
5
Persons Killed
472
▼ -5.6%was 500
Persons Injured
223
▼ -10.8%was 250
Hit-and-Run Crashes
Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 66 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 Chicopee saw a slight year-over-year decline. The total number of incidents fell by 2.3%, from 1,641 in 2024 to 1,604 in 2025. This downward trend was also reflected in total injuries, which decreased by 5.6% from 500 to 472.
223
Hit-and-Run Crashes — 2025
▼ -10.8% vs prior (250)
Hit-and-run incidents showed a downward trend in 2025 compared to the previous year. The total count of hit-and-run crashes decreased from 250 to 223. This represents a decline in the hit-and-run rate from 15.2% of all crashes in 2024 to 13.9% in 2025.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
4
Motorists Killed
0
Other Killed
8
Pedestrians Injured
13
Cyclists Injured
442
Motorists Injured
9
Other 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 shifted slightly between the two periods. The peak day for crashes moved from Friday (266 crashes) in 2024 to Monday (268 crashes) in 2025. While the 4 p.m. hour remained the peak time for collisions in both years, the number of crashes during that hour decreased from 173 to 147.
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
The overall severity of crashes showed a mixed profile year-over-year. The number of fatal crashes was unchanged at 5, though the fatal crash rate per 100 crashes increased slightly from 0.30 to 0.31. The count of serious injury crashes rose from 23 to 28, while possible injury crashes decreased from 111 to 78.
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
A comparison of contributing factors reveals a significant shift in driver behavior. Crashes attributed to inattention increased by 22.3%, from 215 in 2024 to 263 in 2025, making it a more prominent factor. Conversely, crashes with 'no improper driving' cited decreased from 375 to 311. The count of crashes involving 'driving too fast for conditions' also grew from 44 to 58.
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
Crash conditions saw a notable shift related to road surface conditions. While crashes on dry and wet roads decreased, incidents on surfaces with snow, ice, or slush increased from 71 in 2024 to 105 in 2025. The proportion of crashes under daylight conditions remained stable, accounting for 70.8% of crashes in 2025 compared to 69.3% in the prior year.
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
Vehicle and person demographics show a few year-over-year changes. While Honda and Toyota remained the top two vehicle makes involved in crashes, Hyundai moved up to the 4th position, overtaking Nissan. Analysis of persons involved reveals a 27.9% increase in the 16-20 age group, which grew from 308 individuals in 2024 to 394 in 2025.
Top Vehicle Makes (3,025 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
532 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (3,324 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
The distribution of crashes across speed zones changed, with a notable increase in incidents in 25 mph zones, which rose from 532 to 594 crashes. Conversely, crashes in 30 mph zones fell from 439 to 405. The fatal crash profile also shifted, with three fatalities occurring in 25 mph zones in 2025 compared to one in the prior year, while zones with speeds of 40 mph saw two fatal crashes in 2024 and none in 2025.
Fatal crashes by zone: 25 mph: 3 of 594 (0.505%) · 30 mph: 1 of 405 (0.247%) · 65 mph: 1 of 73 (1.37%)
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: CHICOPEE, MA
- Total crash records analyzed: 1,604
- Total persons involved: 3,896
- Total vehicles involved: 3,025
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). "CHICOPEE, 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/chicopee/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