Monthly Traffic Safety Analysis

221 CRASHES IN
QUINCY, MA
JULY 2022

All metrics benchmarked againstJuly 2021

In July 2022, Quincy experienced 221 crashes, a 36.4% increase compared to the 162 crashes recorded in July 2021. Total injuries also rose by 45% from 40 to 58. Notably, the number of fatalities decreased from 1 in July 2021 to 0 in July 2022.

221

36.4%was 162

Total Crash Events

0

-100.0%was 1

Persons Killed

58

45.0%was 40

Persons Injured

18

-21.7%was 23

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in Quincy showed an upward trend year-over-year, with total crashes increasing by 36.4% from 162 in July 2021 to 221 in July 2022. This represents an increase of 59 crashes.

18

Hit-and-Run Crashes — July 2022

-21.7% vs prior (23)

Hit-and-run crashes decreased from 23 in July 2021 to 18 in July 2022. The hit-and-run rate also showed a downward trend, decreasing from 14.2% of total crashes in July 2021 to 8.1% in July 2022.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 1-100.0%

4

Pedestrians Injured

Prior: 333.3%

3

Cyclists Injured

Prior: 0%

51

Motorists Injured

Prior: 3737.8%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · 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 Friday in both periods, with 49 crashes in July 2022 compared to 32 in July 2021. The peak crash hour shifted from 4 PM (21 crashes) in July 2021 to 5 PM (25 crashes) in July 2022. Notably, crashes on Sundays and Wednesdays each doubled, increasing by 100% from 16 to 32 and 17 to 34 crashes respectively.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes decreased from 1 in July 2021 to 0 in July 2022. Serious injuries increased in count from 4 to 5, while their proportion of total crashes slightly decreased from 2.5% to 2.3%. Minor injury crashes saw an increase in count from 19 to 31, and their share of total crashes rose from 11.7% to 14%.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes2.3%
25.0%prior 4
Minor Injury31minor injury crashes14%
63.2%prior 19
Possible Injury12possible injury crashes5.4%
33.3%prior 9
No Injury165no injury crashes74.7%
35.2%prior 122

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · Most severe injury per crash record

Top Contributing Factors

Inattention remained the leading contributing factor, increasing by 25 crashes from 48 in July 2021 to 73 in July 2022, a 52.1% increase in count. Crashes due to "Failed to yield right of way" increased by 12, from 19 to 31, moving it from the fourth to the second most common factor. Conversely, "Followed too closely" crashes decreased by 4, from 19 to 15, dropping its rank from second to fourth.

Officer-Reported Primary Contributing Cause

Inattention73 (33%)52.1%prior 48
Failed to yield right of way31 (14%)63.2%prior 19
No improper driving24 (10.9%)26.3%prior 19
Followed too closely15 (6.8%)-21.1%prior 19
Other improper action10 (4.5%)
Failure to keep in proper lane or running off road9 (4.1%)80.0%prior 5
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner8 (3.6%)0.0%prior 8
Made an improper turn4 (1.8%)
Disregarded traffic signs, signals, road markings4 (1.8%)
Visibility obstructed4 (1.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in clear weather conditions significantly increased from 57.4% (93 crashes) in July 2021 to 93.7% (207 crashes) in July 2022. Correspondingly, crashes on wet road surfaces decreased substantially, from 42 crashes (25.9%) in July 2021 to 4 crashes (1.8%) in July 2022. Crashes in daylight conditions also saw an increase in proportion, rising from 70.4% to 80.5% of total crashes.

Weather

Clear174 (78.7%)
107.1%prior 84
Clear/Clear33 (14.9%)
266.7%prior 9
Cloudy10 (4.5%)
-68.8%prior 32
Clear/Cloudy1 (0.5%)
Cloudy/Cloudy1 (0.5%)
Cloudy/Rain1 (0.5%)
-80.0%prior 5
Rain1 (0.5%)
-95.7%prior 23

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · Weather condition at time of crash

Lighting

Daylight178 (80.5%)
56.1%prior 114
Dark - lighted roadway33 (14.9%)
-13.2%prior 38
Dusk7 (3.2%)
-12.5%prior 8
Dark - roadway not lighted3 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · Lighting condition field

Road Surface

Dry216 (97.7%)
81.5%prior 119
Wet4 (1.8%)
-90.5%prior 42
Sand, mud, dirt, oil, gravel1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · Road surface condition field

Vehicles & Demographics

The age group of persons involved in crashes showed significant shifts, with those aged 21-25 increasing from 40 to 69, and those aged 35-44 nearly doubling from 48 to 93. The 65+ age group also saw a substantial increase, rising from 33 to 64 persons. Toyota remained the top vehicle make involved, increasing from 73 to 79, while Ford moved into second place with 53 vehicles, up from 36.

Top Vehicle Makes (424 vehicles)

1
TOYOTA79 (18.6%)
8.2%prior 73
2
FORD53 (12.5%)
47.2%prior 36
3
HONDA53 (12.5%)
26.2%prior 42
4
NISSAN29 (6.8%)
38.1%prior 21
5
CHEVROLET26 (6.1%)
36.8%prior 19
6
JEEP21 (5%)
50.0%prior 14
7
HYUNDAI15 (3.5%)
66.7%prior 9
8
SUBARU13 (3.1%)
8.3%prior 12
9
GMC11 (2.6%)
10
BMW10 (2.4%)
-16.7%prior 12

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · Vehicle unit records

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

Sex Distribution (521 persons with recorded sex)

Male275 (52.8%)
31.0%prior 210
Female246 (47.2%)
73.2%prior 142

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 25 mph speed zones increased by 42 incidents, from 87 in July 2021 to 129 in July 2022, and this zone saw a decrease from 1 fatality to 0. Incidents in 30 mph zones rose from 24 to 33 crashes, while 55 mph zones experienced an increase from 20 to 23 crashes. Overall, crashes increased across all major speed limit categories.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-07-01 to 2022-07-31 · 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: 2022-07-01 through 2022-07-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-07-01 through 2022-07-31 (31 days)
  • Geographic scope: QUINCY, MA
  • Total crash records analyzed: 221
  • Total persons involved: 563
  • Total vehicles involved: 424

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: July 2022." Published June 21, 2026. Reporting period: 2022-07-01 to 2022-07-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/quincy/july-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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Quincy, MA Crash Report — July 2022 | ThatCarHitMe.com