ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CHELSEA, MA · SEPTEMBER 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/chelsea/september-2023-report
Monthly Traffic Safety Analysis
95 CRASHES IN
CHELSEA, MA
SEPTEMBER 2023
In September 2023, CHELSEA, MA recorded 95 total crashes, an increase of 13.1% from the 84 crashes reported in September 2022. Despite the increase in total crashes, total injuries decreased by 32.5%, from 40 to 27. The most notable shift was a 200% increase in hit-and-run crashes, rising from 2 to 6.
95
▲ 13.1%was 84
Total Crash Events
0
Persons Killed
27
▼ -32.5%was 40
Persons Injured
6
▲ 200.0%was 2
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. 5 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates an increase in total crashes, rising from 84 in September 2022 to 95 in September 2023, a 13.1% increase. Total fatalities remained stable at 0 in both periods, while total injuries decreased significantly from 40 to 27, a 32.5% reduction.
6
Hit-and-Run Crashes — September 2023
▲ 200.0% vs prior (2)
Hit-and-run crashes significantly increased from 2 in September 2022 to 6 in September 2023, representing a 200% rise in count. Concurrently, the hit-and-run rate rose from 2.4% to 6.3% of all crashes, indicating an upward trend.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
6
Pedestrians Injured
1
Cyclists Injured
20
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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 18 crashes in September 2022 to Friday with 19 crashes in September 2023. The peak hour remained 3p in both periods, with the number of crashes at this hour increasing from 7 in September 2022 to 11 in September 2023.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes remained at 0 in both periods. Serious injury crashes decreased from 2 (2.4% of crashes) in September 2022 to 1 (1.1%) in September 2023. The proportion of crashes resulting in any injury (Serious, Minor, or Possible) decreased from 31.0% in September 2022 to 19.0% in September 2023.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record
Top Contributing Factors
The number of crashes attributed to "No improper driving" decreased from 28 to 26, a 7.1% reduction. Crashes involving "Operating vehicle in erratic, reckless, careless, negligent or aggressive manner" increased by 2 crashes, from 4 to 6. "Failed to yield right of way" crashes also increased by 2, from 3 to 5.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring during rain increased from 5 to 10, and cloudy conditions from 3 to 8, indicating a shift towards more crashes in adverse weather. The proportion of crashes on wet roads more than doubled, increasing from 9.5% (8 crashes) in September 2022 to 17.9% (17 crashes) in September 2023. Daylight crashes increased from 55 to 66, while dark-lighted roadway crashes increased from 22 to 25, with their proportions remaining relatively stable.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field
Vehicles & Demographics
HONDA surpassed TOYOTA as the top make involved in crashes, increasing from 33 to 43 vehicles, while TOYOTA decreased from 40 to 36. In terms of persons involved, the 35-44 age group saw a notable increase from 37 to 49, and the 65+ age group doubled from 8 to 16 persons.
Top Vehicle Makes (192 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records
33 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (210 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events
Speed Limit Zones
The 25 mph speed zone continued to account for the highest number of crashes, increasing from 63 to 69. Crashes in the 20 mph zone more than doubled from 3 to 8, and the 45 mph zone saw an increase from 1 to 6 crashes. No fatal crashes were reported in any speed zone for either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-09-01 through 2023-09-30 (30 days)
- Geographic scope: CHELSEA, MA
- Total crash records analyzed: 95
- Total persons involved: 245
- Total vehicles involved: 192
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: September 2023." Published June 21, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/chelsea/september-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2023-09-01 – 2023-09-30
Generated: June 21, 2026 · All rights reserved