Monthly Traffic Safety Analysis

133 CRASHES IN
QUINCY, MA
FEBRUARY 2023

All metrics benchmarked againstFebruary 2022

Total crashes in Quincy decreased by 8.3%, from 145 in February 2022 to 133 in February 2023. The most notable shift was a 90% reduction in speeding-related crashes, which fell from 10 to 1. Additionally, hit-and-run crashes decreased by 50%, from 26 to 13 incidents.

133

-8.3%was 145

Total Crash Events

0

Persons Killed

28

-12.5%was 32

Persons Injured

13

-50.0%was 26

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a decrease in crash incidents, with total crashes falling from 145 to 133, representing an 8.3% reduction. Concurrently, total injuries decreased by 12.5%, from 32 to 28. No fatalities were reported in either the current or prior period.

13

Hit-and-Run Crashes — February 2023

-50.0% vs prior (26)

Hit-and-run crashes decreased by 13 incidents, falling from 26 in the prior period to 13 in the current period. This resulted in the hit-and-run rate decreasing from 17.9% to 9.8% year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

3

Pedestrians Injured

Prior: 250.0%

25

Motorists Injured

Prior: 30-16.7%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Tuesday remained the peak day for crashes, increasing from 24 crashes in February 2022 to 32 crashes in February 2023. The peak crash hour shifted from 2 p.m. in the prior period with 13 crashes to 8 a.m. in the current period with 14 crashes.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at 0 in both periods, and the fatal crash rate was 0%. The number of serious injury crashes increased from 2 to 4, while minor injury crashes remained stable at 13. Overall, total injuries decreased from 32 to 28.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes3%
100.0%prior 2
Minor Injury13minor injury crashes9.8%
0.0%prior 13
Possible Injury8possible injury crashes6%
-11.1%prior 9
No Injury106no injury crashes79.7%
-7.0%prior 114

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Most severe injury per crash record

Top Contributing Factors

The contributing factor "Followed too closely" saw a significant increase in count from 7 to 16 crashes, a 128.6% change. Conversely, "Driving too fast for conditions" decreased in count from 6 crashes to 0, and "Exceeded authorized speed limit" decreased from 3 to 1 crash, a 66.7% change. "Inattention" also increased from 29 to 36 crashes, a 24.1% change.

Officer-Reported Primary Contributing Cause

Inattention36 (27.1%)24.1%prior 29
No improper driving22 (16.5%)-12.0%prior 25
Failed to yield right of way20 (15%)11.1%prior 18
Followed too closely16 (12%)128.6%prior 7
Failure to keep in proper lane or running off road6 (4.5%)-50.0%prior 12
Disregarded traffic signs, signals, road markings6 (4.5%)
Fatigued/asleep3 (2.3%)
Operating defective equipment2 (1.5%)
Over-correcting/over-steering2 (1.5%)
Visibility obstructed2 (1.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

There was a notable shift in road surface conditions, with crashes on dry roads increasing from 81 to 104, while crashes on wet, icy, and snowy surfaces decreased significantly. Crashes on wet roads decreased from 32 to 20, on icy roads from 18 to 6, and on snowy roads from 13 to 3. Weather conditions like clear and cloudy remained consistent in their crash counts across both periods.

Weather

Clear85 (63.9%)
0.0%prior 85
Cloudy15 (11.3%)
0.0%prior 15
Clear/Clear12 (9.0%)
100.0%prior 6
Rain4 (3.0%)
-55.6%prior 9
Snow4 (3.0%)
-42.9%prior 7
Sleet, hail (freezing rain or drizzle)3 (2.3%)
Snow/Cloudy3 (2.3%)
Cloudy/Cloudy2 (1.5%)
Snow/Snow1 (0.8%)
Cloudy/Rain1 (0.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Weather condition at time of crash

Lighting

Daylight77 (58.3%)
-3.8%prior 80
Dark - lighted roadway50 (37.9%)
-13.8%prior 58
Dusk3 (2.3%)
Dark - roadway not lighted1 (0.8%)
Dawn1 (0.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Lighting condition field

Road Surface

Dry104 (78.2%)
28.4%prior 81
Wet20 (15.0%)
-37.5%prior 32
Ice6 (4.5%)
-66.7%prior 18
Snow3 (2.3%)
-76.9%prior 13

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes decreased from 371 to 343. The age group 21-25 saw a substantial decrease in persons involved, from 54 to 32, while the 35-44 age group saw an increase from 45 to 57. Among vehicle makes, HONDA vehicles involved in crashes increased from 25 to 38, while CHEVROLET vehicles decreased from 23 to 12.

Top Vehicle Makes (269 vehicles)

1
TOYOTA58 (21.6%)
-10.8%prior 65
2
HONDA38 (14.1%)
52.0%prior 25
3
FORD32 (11.9%)
18.5%prior 27
4
JEEP16 (5.9%)
14.3%prior 14
5
NISSAN13 (4.8%)
-38.1%prior 21
6
SUBARU12 (4.5%)
0.0%prior 12
7
CHEVROLET12 (4.5%)
-47.8%prior 23
8
HYUNDAI9 (3.3%)
50.0%prior 6
9
ACURA8 (3%)
10
AUDI8 (3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Vehicle unit records

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

Sex Distribution (311 persons with recorded sex)

Male177 (56.9%)
-7.3%prior 191
Female134 (43.1%)
1.5%prior 132

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 25 mph speed zones increased from 60 to 71. Conversely, crashes in lower speed zones of 5 mph and 10 mph both decreased from 7 to 1 crash respectively. All speed zones reported zero fatalities in both periods.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · 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: 2023-02-01 through 2023-02-28
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-02-01 through 2023-02-28 (28 days)
  • Geographic scope: QUINCY, MA
  • Total crash records analyzed: 133
  • Total persons involved: 343
  • Total vehicles involved: 269

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). "QUINCY, MA Crash Intelligence Report: February 2023." Published June 21, 2026. Reporting period: 2023-02-01 to 2023-02-28. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/quincy/february-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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