Monthly Traffic Safety Analysis

72 CRASHES IN
CHELMSFORD, MA
NOVEMBER 2023

All metrics benchmarked againstNovember 2022

CHELMSFORD experienced a 6.5% decrease in total crashes, from 77 in November 2022 to 72 in November 2023. The most significant year-over-year shift was the reduction in total fatalities from 1 to 0. Despite fewer overall crashes, total injuries increased from 26 to 30.

72

-6.5%was 77

Total Crash Events

0

-100.0%was 1

Persons Killed

30

15.4%was 26

Persons Injured

4

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in CHELMSFORD showed a downward trend year-over-year, decreasing by 5 crashes from 77 to 72, a 6.5% reduction. While fatalities were eliminated, total injuries saw an increase of 4, rising from 26 to 30.

4

Hit-and-Run Crashes — November 2023

5.6% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1-100.0%

30

Motorists Injured

Prior: 2615.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · 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 Tuesday in November 2022, which saw 27 crashes, to Thursday in November 2023, with 17 crashes. Similarly, the peak hour changed from 5 PM (8 crashes) in the prior year to 2 PM (8 crashes) in the current year, maintaining the same peak count.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes decreased from 1 in November 2022 to 0 in November 2023. Total injuries increased by 4, from 26 to 30, despite a decrease in overall crashes. The proportion of crashes resulting in 'No Injury' increased from 71.4% in the prior period to 75.0% in the current period.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.4%
Minor Injury11minor injury crashes15.3%
-15.4%prior 13
Possible Injury6possible injury crashes8.3%
-14.3%prior 7
No Injury54no injury crashes75%
-1.8%prior 55

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, 'Followed too closely', decreased by 6 crashes, from 21 in November 2022 to 15 in November 2023. 'No improper driving' also saw a decrease of 7 crashes, from 18 to 11. Conversely, 'Failure to keep in proper lane or running off road' increased by 4 crashes, from 3 to 7.

Officer-Reported Primary Contributing Cause

Followed too closely15 (20.8%)-28.6%prior 21
No improper driving11 (15.3%)-38.9%prior 18
Failed to yield right of way7 (9.7%)-12.5%prior 8
Failure to keep in proper lane or running off road7 (9.7%)
Inattention6 (8.3%)
Disregarded traffic signs, signals, road markings5 (6.9%)
Driving too fast for conditions4 (5.6%)
Over-correcting/over-steering2 (2.8%)
Distracted2 (2.8%)
Glare1 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes on 'Dry' road surfaces decreased by 11, from 66 to 55, while those on 'Wet' surfaces increased by 4, from 11 to 15. Crashes during 'Daylight' conditions increased by 12, from 35 to 47, but crashes in 'Dark - lighted roadway' decreased by 11, from 22 to 11. The number of crashes occurring in 'Clear' weather decreased by 12, from 28 to 16.

Weather

Clear/Clear31 (43.1%)
6.9%prior 29
Clear16 (22.2%)
-42.9%prior 28
Cloudy5 (6.9%)
0.0%prior 5
Cloudy/Rain4 (5.6%)
Clear/Cloudy4 (5.6%)
Cloudy/Cloudy4 (5.6%)
Rain3 (4.2%)
-40.0%prior 5
Cloudy/Clear1 (1.4%)
Cloudy/Unknown1 (1.4%)
Rain/Cloudy1 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Weather condition at time of crash

Lighting

Daylight47 (65.3%)
34.3%prior 35
Dark - lighted roadway11 (15.3%)
-50.0%prior 22
Dark - roadway not lighted8 (11.1%)
-33.3%prior 12
Dawn2 (2.8%)
-60.0%prior 5
Dusk2 (2.8%)
Other1 (1.4%)
Dark - unknown roadway lighting1 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Lighting condition field

Road Surface

Dry55 (77.5%)
-16.7%prior 66
Wet15 (21.1%)
36.4%prior 11
Slush1 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes increased from 170 to 176 year-over-year. The 26-34 age group saw a notable increase in involvement, from 22 to 36 persons, while the 35-44 age group decreased from 30 to 22. Toyota became the most frequently involved vehicle make, with its count rising from 18 to 29, surpassing Honda which decreased from 25 to 18.

Top Vehicle Makes (138 vehicles)

1
TOYOTA29 (21%)
61.1%prior 18
2
HONDA18 (13%)
-28.0%prior 25
3
FORD11 (8%)
-8.3%prior 12
4
NISSAN10 (7.2%)
42.9%prior 7
5
CHEVROLET10 (7.2%)
66.7%prior 6
6
KIA8 (5.8%)
7
HYUNDAI4 (2.9%)
8
GMC4 (2.9%)
-20.0%prior 5
9
JEEP4 (2.9%)
-42.9%prior 7
10
SUBARU4 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Vehicle unit records

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

Sex Distribution (164 persons with recorded sex)

Male84 (51.2%)
-18.4%prior 103
Female80 (48.8%)
50.9%prior 53

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 30 mph speed limit zone significantly increased from 10 in November 2022 to 24 in November 2023. The 65 mph zone experienced a decrease in crashes from 18 to 14, and notably, it reported 0 fatalities in the current period compared to 1 fatality in the prior period. Crashes in the 35 mph zone decreased from 12 to 10.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · 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: 2023-11-01 through 2023-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-11-01 through 2023-11-30 (30 days)
  • Geographic scope: CHELMSFORD, MA
  • Total crash records analyzed: 72
  • Total persons involved: 176
  • Total vehicles involved: 138

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). "CHELMSFORD, MA Crash Intelligence Report: November 2023." Published June 21, 2026. Reporting period: 2023-11-01 to 2023-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/chelmsford/november-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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Chelmsford, MA Crash Report — November 2023 | ThatCarHitMe.com