ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CHICOPEE, MA · 2023
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/2023-annual-report
Yearly Traffic Safety Analysis
1,818 CRASHES IN
CHICOPEE, MA
2023
In Chicopee, total traffic crashes increased by 2.2%, from 1,779 in 2022 to 1,818 in 2023. While total crashes and injuries saw a slight rise, the number of fatalities decreased from 12 to 8. The most significant year-over-year change was a 39.4% increase in hit-and-run incidents, which grew from 218 to 304.
1,818
▲ 2.2%was 1,779
Total Crash Events
8
▼ -33.3%was 12
Persons Killed
561
▲ 1.8%was 551
Persons Injured
304
▲ 39.4%was 218
Hit-and-Run Crashes
Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 87 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic safety trends showed a slight degradation, with total crashes rising 2.2% from 1,779 to 1,818 year-over-year. Total injuries also increased by 1.8%, from 551 to 561. However, the most severe outcomes improved, as total fatalities resulting from crashes declined by 33.3%, from 12 in 2022 to 8 in 2023.
304
Hit-and-Run Crashes — 2023
▲ 39.4% vs prior (218)
Hit-and-run incidents increased substantially between the two periods. The number of hit-and-run crashes rose by 39.4%, from 218 in 2022 to 304 in 2023. This trend also saw the hit-and-run rate, or the proportion of all crashes that were hit-and-runs, increase from 12.3% to 16.7% year-over-year.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
7
Motorists Killed
0
Other Killed
27
Pedestrians Injured
16
Cyclists Injured
517
Motorists Injured
1
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes were largely consistent, with the afternoon rush hour being the most common time for incidents. The peak hour for crashes was 4 PM in both 2023 (165 crashes) and 2022 (167 crashes). A notable shift occurred in the peak day for crashes, which moved from Tuesday in 2022 (290 crashes) to Friday in 2023 (308 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes increased, the overall severity profile showed a slight improvement. The number of fatal crashes decreased from 11 in 2022 to 6 in 2023, and their share of all crashes fell from 0.6% to 0.3%. Crashes resulting in minor injuries increased from 243 to 269, making up a larger share of the total (14.8% in 2023 vs. 13.7% in 2022), while serious injury crashes saw a small decline from 30 to 27.
Severity is per crash event (most severe injury). 6 fatal crash events resulted in 8 persons killed.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors to crashes remained consistent, with 'Inattention' and 'Followed too closely' being the top driver-related causes in both years. The counts for these factors saw minor increases, with 'Followed too closely' incidents rising from 176 to 188. A more significant change was observed in crashes attributed to 'Disregarded traffic signs, signals, road markings,' which saw a 43.8% increase in count, from 48 incidents in 2022 to 69 in 2023.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Environmental conditions at the time of crashes were largely similar between the two periods, with the majority of incidents in both years occurring in daylight on dry roads. In 2023, 79.6% of crashes occurred on dry surfaces, compared to 78.4% in 2022. There was a slight increase in the proportion of crashes happening on wet roads (16.9% in 2023 vs. 15.1% in 2022) and during rain (6.7% vs. 5.1%).
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The vehicle and demographic profiles of those involved in crashes showed high consistency year-over-year. The top three vehicle makes involved in crashes remained Honda, Toyota, and Ford in both 2022 and 2023, with only minor fluctuations in their respective counts. Similarly, the 26-34 age group represented the largest cohort of individuals involved in crashes in both periods, accounting for 16.8% of persons in 2023 and 17.1% in 2022.
Top Vehicle Makes (3,463 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
649 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (3,762 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones saw an increase in lower-speed urban areas, with crashes in 25 mph zones rising from 532 to 613. There was a notable shift in where fatal crashes occurred; in 2022, the majority of fatal crashes (8 of 11) were in zones with speed limits of 30 mph or less. In 2023, this pattern reversed, with most fatal crashes (5 of 6) occurring in zones with speed limits of 35 mph or higher.
Fatal crashes by zone: 30 mph: 1 of 512 (0.195%) · 35 mph: 2 of 180 (1.111%) · 55 mph: 2 of 113 (1.77%) · 65 mph: 1 of 83 (1.205%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: CHICOPEE, MA
- Total crash records analyzed: 1,818
- Total persons involved: 4,466
- Total vehicles involved: 3,463
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: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/chicopee/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved