Monthly Traffic Safety Analysis

43 CRASHES IN
WAKEFIELD, MA
JUNE 2023

All metrics benchmarked againstJune 2022

In June 2023, Wakefield experienced 43 crashes, a significant increase from the 24 crashes reported in June 2022, marking a 79.17% rise. Total injuries also saw a substantial increase, jumping from 2 to 11 year-over-year. The most notable shift was the 700% increase in 'Followed too closely' as a contributing factor, rising from 1 to 8 crashes.

43

79.2%was 24

Total Crash Events

0

Persons Killed

11

450.0%was 2

Persons Injured

5

25.0%was 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. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Wakefield indicates a substantial upward trend year-over-year, with total crashes increasing from 24 in June 2022 to 43 in June 2023. This represents an increase of 19 crashes, or 79.17%.

5

Hit-and-Run Crashes — June 2023

25.0% vs prior (4)

The number of hit-and-run crashes increased from 4 in June 2022 to 5 in June 2023. Despite this increase in count, the overall hit-and-run rate decreased from 16.7% of total crashes in the prior period to 11.6% in the current period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

11

Motorists Injured

Prior: 2450.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · 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 Friday with 8 crashes in June 2022 to Thursday with 12 crashes in June 2023. The peak hour also shifted, from 2 p.m. with 6 crashes in the prior period to 4 p.m. with 6 crashes in the current period, indicating a change in the busiest times for incidents.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes in either period. Total injuries saw a significant increase from 2 in June 2022 to 11 in June 2023. Serious injury crashes, with 1 incident (2.3% of total crashes), appeared in the current period, whereas none were reported in the prior period, and minor injury crashes increased from 1 (4.2%) to 5 (11.6%).

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes2.3%
Minor Injury5minor injury crashes11.6%
400.0%prior 1
Possible Injury2possible injury crashes4.7%
100.0%prior 1
No Injury33no injury crashes76.7%
50.0%prior 22

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'Followed too closely' increased from 1 to 8, a 700% increase in count, making it the top contributing factor in June 2023. 'Failed to yield right of way' also saw a 600% increase in count, rising from 1 to 7 crashes. Conversely, crashes with 'No improper driving' as a factor decreased from 12 to 6, a 50% decrease in count, shifting its ranking significantly.

Officer-Reported Primary Contributing Cause

Followed too closely8 (18.6%)
Failed to yield right of way7 (16.3%)
Inattention7 (16.3%)
No improper driving6 (14%)-50.0%prior 12
Physical impairment2 (4.7%)
Failure to keep in proper lane or running off road2 (4.7%)
Over-correcting/over-steering2 (4.7%)
Made an improper turn1 (2.3%)
Glare1 (2.3%)
Exceeded authorized speed limit1 (2.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather increased from 19 to 31 year-over-year, while those in cloudy conditions rose from 4 to 7. Incidents during dark-lighted roadway conditions also saw a notable increase, from 1 crash in June 2022 to 8 crashes in June 2023. The road surface data for the prior period was not available for comparison.

Weather

Clear31 (72.1%)
63.2%prior 19
Cloudy7 (16.3%)
Rain3 (7.0%)
Cloudy/Rain2 (4.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · Weather condition at time of crash

Lighting

Daylight34 (79.1%)
47.8%prior 23
Dark - lighted roadway8 (18.6%)
Dawn1 (2.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · Lighting condition field

Road Surface

Dry38 (88.4%)
Wet5 (11.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (88 vehicles)

1
TOYOTA16 (18.2%)
60.0%prior 10
2
HONDA16 (18.2%)
77.8%prior 9
3
CHEVROLET6 (6.8%)
4
NISSAN6 (6.8%)
0.0%prior 6
5
FORD6 (6.8%)
6
JEEP5 (5.7%)
7
HYUNDAI4 (4.5%)
8
VOLVO3 (3.4%)
9
GMC2 (2.3%)
10
CHRYSLER2 (2.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · Vehicle unit records

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

Sex Distribution (95 persons with recorded sex)

Male53 (55.8%)
71.0%prior 31
Female42 (44.2%)
55.6%prior 27

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · Person-level records linked to crash events

Speed Limit Zones

No fatal crashes were reported in any speed zone during either period. Crashes in 30 mph zones increased from 15 to 21, and those in 55 mph zones saw a substantial rise from 3 to 15. Speed zones of 5 mph and 65 mph, present in the prior period, did not report any crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-06-01 to 2023-06-30 · 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: 2023-06-01 through 2023-06-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-06-01 through 2023-06-30 (30 days)
  • Geographic scope: WAKEFIELD, MA
  • Total crash records analyzed: 43
  • Total persons involved: 104
  • Total vehicles involved: 88

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). "WAKEFIELD, MA Crash Intelligence Report: June 2023." Published June 21, 2026. Reporting period: 2023-06-01 to 2023-06-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/wakefield/june-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

Wakefield, MA Crash Report — June 2023 | ThatCarHitMe.com