Monthly Traffic Safety Analysis

155 CRASHES IN
CHICOPEE, MA
DECEMBER 2023

All metrics benchmarked againstDecember 2022

Total crashes in Chicopee decreased by 19.7% from 193 in December 2022 to 155 in December 2023. The most notable shift was the reduction in total fatalities from 2 in the prior period to 0 in the current period.

155

▼ -19.7%was 193

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

43

▼ -4.4%was 45

Persons Injured

22

▼ -4.3%was 23

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. 7 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a decrease in crash incidents year-over-year, with total crashes falling from 193 to 155. This represents a 19.7% reduction in crashes. Additionally, total fatalities decreased from 2 to 0.

22

Hit-and-Run Crashes — December 2023

▼ -4.3% vs prior (23)

The number of hit-and-run crashes decreased slightly from 23 to 22. However, the hit-and-run rate increased from 11.9% in the prior period to 14.2% in the current period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 1↓-100.0%

6

Pedestrians Injured

Prior: 2↑200.0%

37

Motorists Injured

Prior: 42↓-11.9%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Thursday (35 crashes) in the prior period to Friday (34 crashes) in the current period. The peak hour for crashes also changed, moving from 5 PM (26 crashes) to 4 PM (16 crashes) year-over-year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The fatal crash rate decreased from 1.04% in the prior period (2 fatal crashes) to 0% in the current period (0 fatal crashes). Serious injuries (severity 'A') decreased from 3 crashes (1.6% share) to 1 crash (0.6% share). While minor injuries increased from 18 to 21 crashes, possible injuries decreased from 18 to 9 crashes.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes0.6%
↓-66.7%prior 3
Minor Injury21minor injury crashes13.5%
↑16.7%prior 18
Possible Injury9possible injury crashes5.8%
↓-50.0%prior 18
No Injury117no injury crashes75.5%
↓-18.2%prior 143

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, 'No improper driving', decreased by 15 crashes, from 51 to 36, though its share remained the highest at 23.2% in the current period. 'Failure to keep in proper lane or running off road' saw the largest increase in count, rising from 4 crashes to 14 crashes. Conversely, 'Followed too closely' decreased by 5 crashes, from 15 to 10.

Officer-Reported Primary Contributing Cause

No improper driving36 (23.2%)↓-29.4%prior 51
Inattention23 (14.8%)↓-4.2%prior 24
Failed to yield right of way21 (13.5%)↑16.7%prior 18
Failure to keep in proper lane or running off road14 (9%)
Followed too closely10 (6.5%)↓-33.3%prior 15
Disregarded traffic signs, signals, road markings8 (5.2%)
Other improper action8 (5.2%)↓-38.5%prior 13
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner6 (3.9%)↓-33.3%prior 9
Driving too fast for conditions5 (3.2%)↓-44.4%prior 9
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway4 (2.6%)↓-50.0%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather decreased from 93 to 86, while those in 'Rain' increased from 11 to 19. The number of crashes on 'Dry' road surfaces decreased from 112 to 105, whereas crashes on 'Wet' surfaces saw a slight increase from 48 to 49. Crashes occurring in 'Dark - lighted roadway' conditions decreased from 80 to 51.

Weather

Clear86 (55.8%)
↓-7.5%prior 93
Cloudy20 (13.0%)
↓-13.0%prior 23
Rain19 (12.3%)
↑72.7%prior 11
Clear/Cloudy6 (3.9%)
↓-50.0%prior 12
Cloudy/Rain5 (3.2%)
↓-68.8%prior 16
Rain/Cloudy4 (2.6%)
Cloudy/Unknown2 (1.3%)
Clear/Other2 (1.3%)
Clear/Rain2 (1.3%)
Clear/Unknown2 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight90 (58.4%)
↓-7.2%prior 97
Dark - lighted roadway51 (33.1%)
↓-36.3%prior 80
Dark - roadway not lighted6 (3.9%)
→0.0%prior 6
Dusk5 (3.2%)
Dark - unknown roadway lighting2 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry105 (68.2%)
↓-6.3%prior 112
Wet49 (31.8%)
↑2.1%prior 48

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 351 to 294. Honda became the top vehicle make involved, with 33 vehicles, despite a decrease from 46 in the prior period, while Toyota decreased from 49 to 32. The 16-20 age group saw a decrease in representation from 38 persons to 19 persons, while the 0-15 age group increased from 23 to 30 persons.

Top Vehicle Makes (294 vehicles)

1
HONDA33 (11.2%)
↓-28.3%prior 46
2
TOYOTA32 (10.9%)
↓-34.7%prior 49
3
HYUNDAI30 (10.2%)
↑20.0%prior 25
4
FORD29 (9.9%)
↓-19.4%prior 36
5
NISSAN27 (9.2%)
↓-10.0%prior 30
6
CHEVROLET21 (7.1%)
↓-27.6%prior 29
7
JEEP14 (4.8%)
↑55.6%prior 9
8
SUBARU11 (3.7%)
↓-15.4%prior 13
9
DODGE9 (3.1%)
→0.0%prior 9
10
GMC9 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Vehicle unit records

40 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (329 persons with recorded sex)

Male184 (55.9%)
↓-7.5%prior 199
Female145 (44.1%)
↓-18.5%prior 178

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Person-level records linked to crash events

Speed Limit Zones

There were no fatal crashes reported in any speed zone in the current period, compared to 2 fatal crashes in the prior period (one at 30 mph and one at 55 mph). Crashes at the 25 mph speed limit decreased from 58 to 52, and crashes at 30 mph decreased from 55 to 47. The 35 mph speed limit was the only one to see an increase in crashes, rising from 16 to 17.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 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: 2023-12-01 through 2023-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-12-01 through 2023-12-31 (31 days)
  • Geographic scope: CHICOPEE, MA
  • Total crash records analyzed: 155
  • Total persons involved: 372
  • Total vehicles involved: 294

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: December 2023." Published June 21, 2026. Reporting period: 2023-12-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/chicopee/december-2023-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

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Chicopee, MA Crash Report — December 2023 | ThatCarHitMe.com