ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CHELSEA, MA · DECEMBER 2022
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/chelsea/december-2022-report
Monthly Traffic Safety Analysis
93 CRASHES IN
CHELSEA, MA
DECEMBER 2022
Total crashes in CHELSEA, MA increased significantly from 56 in December 2021 to 93 in December 2022, representing a 66.1% rise. The most notable year-over-year shift was in hit-and-run incidents, which quadrupled from 1 to 4 crashes. Total injuries also surged by 200%, from 13 to 39 persons.
93
▲ 66.1%was 56
Total Crash Events
0
Persons Killed
39
▲ 200.0%was 13
Persons Injured
4
▲ 300.0%was 1
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 · 2022-12-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a substantial increase in crash activity year-over-year. Total crashes rose by 66.1%, from 56 in December 2021 to 93 in December 2022. Concurrently, total injuries surged by 200%, increasing from 13 to 39 persons.
4
Hit-and-Run Crashes — December 2022
▲ 300.0% vs prior (1)
Hit-and-run crashes increased significantly year-over-year, rising from 1 crash in December 2021 to 4 crashes in December 2022. This represents a 300% increase in the count of hit-and-run incidents, with the hit-and-run rate rising from 1.8% to 4.3% of all crashes.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
3
Pedestrians Injured
1
Cyclists Injured
35
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · 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 Friday in December 2021 (14 crashes) to Sunday in December 2022 (19 crashes). The peak hour for crashes also changed, moving from 4 p.m. (7 crashes) in the prior period to 6 p.m. (12 crashes) in the current period, indicating a shift in the most frequent times for crash occurrences.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While no fatalities occurred in either period, the number of injury crashes increased from 12 in December 2021 to 23 in December 2022, a 91.7% rise. Notably, serious injury (A) crashes, which were absent in the prior period, accounted for 3 crashes in the current period. Total injuries to persons increased by 200%, from 13 to 39.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
The most frequently cited contributing factor, "No improper driving," increased from 19 crashes in the prior period to 29 in the current period. "Operating vehicle in erratic, reckless, careless, negligent or aggressive manner" saw a 150% increase in count, rising from 2 to 5 crashes. Conversely, "Failed to yield right of way" decreased by 50% in count, from 4 crashes to 2 crashes.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes under "Clear" weather conditions increased from 39 to 60, while crashes in "Rain" conditions more than tripled from 4 to 13. Crashes occurring in "Dark - lighted roadway" conditions increased by 91.7%, from 24 to 46, and crashes on "Wet" road surfaces more than doubled, rising from 11 to 26.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased by 51.2%, from 123 to 186. Toyota and Honda remained the top two vehicle makes involved, with Toyota increasing from 28 to 44 and Honda from 20 to 36. The 26-34 age group saw a 110% increase in persons involved, rising from 20 to 42, and the 55-64 age group increased by 146.7%, from 15 to 37.
Top Vehicle Makes (186 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Vehicle unit records
22 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (200 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
There were no fatal crashes reported in any speed zone during either period. The 25 mph speed zone experienced the largest increase in crash count, rising from 34 crashes in December 2021 to 73 crashes in December 2022, an increase of 39 crashes. Crashes in the 20 mph zone decreased from 8 to 3.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · 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: 2022-12-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-12-01 through 2022-12-31 (31 days)
- Geographic scope: CHELSEA, MA
- Total crash records analyzed: 93
- Total persons involved: 225
- Total vehicles involved: 186
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). "CHELSEA, MA Crash Intelligence Report: December 2022." Published June 21, 2026. Reporting period: 2022-12-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/chelsea/december-2022-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: 2022-12-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved