Monthly Traffic Safety Analysis

68 CRASHES IN
CHELSEA, MA
OCTOBER 2022

All metrics benchmarked againstOctober 2021

Total crashes in CHELSEA, MA decreased by 17.1% year-over-year, from 82 crashes in October 2021 to 68 crashes in October 2022. This period also saw a significant 50% reduction in total injuries, falling from 38 to 19. Fatalities remained at zero in both comparative periods.

68

-17.1%was 82

Total Crash Events

0

Persons Killed

19

-50.0%was 38

Persons Injured

2

-33.3%was 3

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a decrease in crash incidents and injuries year-over-year for CHELSEA, MA. Total crashes fell by 17.1%, from 82 in October 2021 to 68 in October 2022. Concurrently, total injuries decreased by 50%, from 38 to 19.

2

Hit-and-Run Crashes — October 2022

-33.3% vs prior (3)

Hit-and-run crashes decreased from 3 in October 2021 to 2 in October 2022. Consequently, the hit-and-run rate also saw a decrease, falling from 3.7% to 2.9% year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

2

Pedestrians Injured

Prior: 3-33.3%

17

Motorists Injured

Prior: 35-51.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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 Monday with 15 incidents in October 2021 to Sunday with 13 incidents in October 2022. The peak hour also changed, moving from 4 p.m. with 10 crashes in the prior period to 8 p.m. with 7 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes or fatalities reported in either October 2021 or October 2022. Total injuries decreased significantly by 50%, from 38 to 19. While serious injury crashes increased from 1 to 2, minor injury crashes saw a substantial decrease from 17 to 4.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes2.9%
100.0%prior 1
Minor Injury4minor injury crashes5.9%
-76.5%prior 17
Possible Injury6possible injury crashes8.8%
20.0%prior 5
No Injury52no injury crashes76.5%
-8.8%prior 57

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' decreased from 25 in the prior period to 22 in the current period. 'Failed to yield right of way' remained constant at 4 crashes in both periods. 'Made an improper turn' crashes increased from 1 to 2, while 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' decreased from 3 to 2.

Officer-Reported Primary Contributing Cause

No improper driving22 (32.4%)-12.0%prior 25
Failed to yield right of way4 (5.9%)
Made an improper turn2 (2.9%)
Driving too fast for conditions2 (2.9%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (2.9%)
Other improper action2 (2.9%)
Followed too closely1 (1.5%)
Over-correcting/over-steering1 (1.5%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (1.5%)
Failure to keep in proper lane or running off road1 (1.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased from 58 to 52, and 'Rain' conditions saw a drop from 10 to 7 crashes. Crashes during 'Daylight' decreased from 49 to 37, while those at 'Dawn' increased from 2 to 4. Crashes on 'Dry' road surfaces decreased from 66 to 52, and 'Wet' road crashes decreased slightly from 16 to 15.

Weather

Clear52 (77.6%)
-10.3%prior 58
Rain7 (10.4%)
-30.0%prior 10
Cloudy3 (4.5%)
-62.5%prior 8
Rain/Cloudy2 (3.0%)
Cloudy/Rain1 (1.5%)
Clear/Unknown1 (1.5%)
Clear/Cloudy1 (1.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Weather condition at time of crash

Lighting

Daylight37 (55.2%)
-24.5%prior 49
Dark - lighted roadway26 (38.8%)
-10.3%prior 29
Dawn4 (6.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Lighting condition field

Road Surface

Dry52 (77.6%)
-21.2%prior 66
Wet15 (22.4%)
-6.3%prior 16

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes decreased from 201 to 165 year-over-year. The number of male persons involved decreased from 106 to 85, while female persons involved remained stable at 60. Honda became the top vehicle make involved, increasing from 25 to 30, while Toyota decreased from 35 to 25.

Top Vehicle Makes (134 vehicles)

1
HONDA30 (22.4%)
20.0%prior 25
2
TOYOTA25 (18.7%)
-28.6%prior 35
3
FORD13 (9.7%)
44.4%prior 9
4
NISSAN7 (5.2%)
-50.0%prior 14
5
CHEVROLET7 (5.2%)
-12.5%prior 8
6
MERCEDES-BENZ6 (4.5%)
0.0%prior 6
7
JEEP5 (3.7%)
-54.5%prior 11
8
HYUNDAI5 (3.7%)
9
BMW4 (3%)
-20.0%prior 5
10
MAZDA4 (3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Vehicle unit records

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

Sex Distribution (145 persons with recorded sex)

Male85 (58.6%)
-19.8%prior 106
Female60 (41.4%)
0.0%prior 60

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 25 mph speed zones decreased from 56 to 54, and 30 mph zones saw a decrease from 4 to 1 crash. Crashes in 35 mph zones decreased from 5 to 2, and 40 mph zones also decreased from 5 to 2. There were no fatal crashes recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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: 2022-10-01 through 2022-10-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-10-01 through 2022-10-31 (31 days)
  • Geographic scope: CHELSEA, MA
  • Total crash records analyzed: 68
  • Total persons involved: 165
  • Total vehicles involved: 134

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: October 2022." Published June 21, 2026. Reporting period: 2022-10-01 to 2022-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/chelsea/october-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

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Chelsea, MA Crash Report — October 2022 | ThatCarHitMe.com