Monthly Traffic Safety Analysis

91 CRASHES IN
CHELSEA, MA
OCTOBER 2024

All metrics benchmarked againstOctober 2023

In October 2024, CHELSEA, MA recorded 91 total crashes, a slight increase from 89 crashes in October 2023, representing a 2.25% rise year-over-year. The most significant shift was in fatalities, which increased from 0 in the prior year to 1 in the current period. Total injuries also rose from 26 to 30.

91

2.2%was 89

Total Crash Events

1

Persons Killed

30

15.4%was 26

Persons Injured

2

-66.7%was 6

Hit-and-Run Crashes

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows a slight increase in total crashes, rising from 89 in October 2023 to 91 in October 2024. This 2.25% increase in crashes was accompanied by a notable increase in fatalities from 0 to 1, and total injuries from 26 to 30.

2

Hit-and-Run Crashes — October 2024

-66.7% vs prior (6)

The number of hit-and-run crashes decreased significantly from 6 in October 2023 to 2 in October 2024. Consequently, the hit-and-run crash rate declined from 6.7% in the prior period to 2.2% in the current period, indicating a downward trend.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 0%

0

Other Killed

Prior: 00.0%

5

Pedestrians Injured

Prior: 366.7%

2

Cyclists Injured

Prior: 1100.0%

21

Motorists Injured

Prior: 22-4.5%

2

Other Injured

Prior: 0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Wednesday in both periods, with 18 crashes in October 2024 and 17 in October 2023. However, the peak hour shifted from 6 p.m. with 10 crashes in October 2023 to 3 p.m. with 11 crashes in October 2024.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes increased from 0 in October 2023 to 1 in October 2024, resulting in a fatal crash rate increase from 0% to 1.1%. Minor injury crashes also saw a significant rise, increasing from 8 in the prior period to 15 in the current period, while serious injury crashes remained stable at 1 for both periods.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.1%
Serious Injury1serious injury crashes1.1%
0.0%prior 1
Minor Injury15minor injury crashes16.5%
87.5%prior 8
Possible Injury7possible injury crashes7.7%
16.7%prior 6
No Injury65no injury crashes71.4%
-7.1%prior 70

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' decreased slightly from 32 in October 2023 to 31 in October 2024. 'Failed to yield right of way' saw a decrease of 4 crashes, from 7 to 3, while 'Followed too closely' increased from 2 to 3 crashes. 'Exceeded authorized speed limit' decreased from 2 crashes to 1.

Officer-Reported Primary Contributing Cause

No improper driving31 (34.1%)-3.1%prior 32
Followed too closely3 (3.3%)
Failed to yield right of way3 (3.3%)-57.1%prior 7
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (2.2%)
Inattention2 (2.2%)
Failure to keep in proper lane or running off road2 (2.2%)
Distracted2 (2.2%)
Visibility obstructed1 (1.1%)
Exceeded authorized speed limit1 (1.1%)
Made an improper turn1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Clear weather conditions remained the most common, with 73 crashes in October 2024 compared to 70 in October 2023. Crashes occurring in rainy conditions significantly decreased from 7 in October 2023 to 1 in October 2024. Similarly, crashes on wet road surfaces decreased from 14 to 3 year-over-year.

Weather

Clear73 (80.2%)
4.3%prior 70
Cloudy7 (7.7%)
40.0%prior 5
Clear/Clear6 (6.6%)
Clear/Unknown2 (2.2%)
Clear/Cloudy1 (1.1%)
Cloudy/Clear1 (1.1%)
Rain1 (1.1%)
-85.7%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Weather condition at time of crash

Lighting

Daylight52 (57.1%)
-5.5%prior 55
Dark - lighted roadway31 (34.1%)
29.2%prior 24
Dusk4 (4.4%)
-42.9%prior 7
Dark - roadway not lighted2 (2.2%)
Dark - unknown roadway lighting1 (1.1%)
Dawn1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Lighting condition field

Road Surface

Dry87 (96.7%)
16.0%prior 75
Wet3 (3.3%)
-78.6%prior 14

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 175 in October 2023 to 186 in October 2024. Honda vehicles involved in crashes rose from 25 to 36, becoming the top make, while Toyota saw a slight decrease from 35 to 34. The 26-34 age group experienced an increase in persons involved, from 39 to 48, and female involvement rose from 63 to 75.

Top Vehicle Makes (186 vehicles)

1
HONDA36 (19.4%)
44.0%prior 25
2
TOYOTA34 (18.3%)
-2.9%prior 35
3
FORD20 (10.8%)
17.6%prior 17
4
CHEVROLET12 (6.5%)
20.0%prior 10
5
HYUNDAI9 (4.8%)
50.0%prior 6
6
NISSAN9 (4.8%)
28.6%prior 7
7
DODGE5 (2.7%)
8
ACURA4 (2.2%)
9
KIA4 (2.2%)
10
BMW4 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Vehicle unit records

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

Sex Distribution (201 persons with recorded sex)

Male126 (62.7%)
-9.4%prior 139
Female75 (37.3%)
19.0%prior 63

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 25 mph zones increased from 62 in October 2023 to 67 in October 2024. Notably, the 50 mph speed zone recorded 1 fatal crash in October 2024 among 4 total crashes, whereas in October 2023, there were 0 fatalities among 1 crash in that zone. Crashes in 45 mph zones decreased from 7 to 2.

Fatal crashes by zone: 50 mph: 1 of 4 (25%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · 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: 2024-10-01 through 2024-10-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-10-01 through 2024-10-31 (31 days)
  • Geographic scope: CHELSEA, MA
  • Total crash records analyzed: 91
  • Total persons involved: 232
  • 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: October 2024." Published June 21, 2026. Reporting period: 2024-10-01 to 2024-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/chelsea/october-2024-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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