Monthly Traffic Safety Analysis

51 CRASHES IN
WAKEFIELD, MA
AUGUST 2023

All metrics benchmarked againstAugust 2022

In August 2023, Wakefield experienced a substantial increase in total crashes, rising to 51 from 30 in August 2022, marking a 70% increase year-over-year. Despite this significant rise in crash events, the total number of injuries decreased slightly from 13 to 12. Notably, both pedestrian and bicycle crashes, which were absent in the prior period, each accounted for 2 crashes in the current period.

51

70.0%was 30

Total Crash Events

0

Persons Killed

12

-7.7%was 13

Persons Injured

3

200.0%was 1

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 · 2023-08-01 to 2023-08-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a notable increase in crash frequency, with total crashes rising by 70% from 30 in August 2022 to 51 in August 2023. Conversely, total injuries saw a slight decrease of 7.7%, moving from 13 to 12 over the same period.

3

Hit-and-Run Crashes — August 2023

200.0% vs prior (1)

Hit-and-run crashes increased from 1 in August 2022 to 3 in August 2023. This change led to an increase in the hit-and-run rate from 3.3% to 5.9% year-over-year, indicating an upward trend.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

2

Pedestrians Injured

Prior: 0%

2

Cyclists Injured

Prior: 1100.0%

8

Motorists Injured

Prior: 12-33.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · 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 Wednesday in both periods, increasing from 10 crashes in August 2022 to 12 crashes in August 2023. The peak hour for crashes shifted from 5 PM in August 2022 to 2 PM in August 2023, with both hours recording 6 crashes.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes reported in either August 2022 or August 2023. Total injuries decreased from 13 to 12 year-over-year, with serious injuries remaining constant at 1 in both periods. Minor injuries increased from 7 to 8, while possible injuries decreased from 3 to 2, and crashes with no injury increased from 17 to 38.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes2%
0.0%prior 1
Minor Injury8minor injury crashes15.7%
14.3%prior 7
Possible Injury2possible injury crashes3.9%
-33.3%prior 3
No Injury38no injury crashes74.5%
123.5%prior 17

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, 'Inattention,' saw a significant increase in count from 4 crashes in August 2022 to 10 crashes in August 2023. 'No improper driving' also increased in count from 7 to 9 crashes, though its share of total crashes decreased from 23.3% to 17.6%. 'Failed to yield right of way' and 'Other improper action' both increased from 2 crashes to 5 crashes year-over-year.

Officer-Reported Primary Contributing Cause

Inattention10 (19.6%)
No improper driving9 (17.6%)28.6%prior 7
Other improper action5 (9.8%)
Failed to yield right of way5 (9.8%)
Failure to keep in proper lane or running off road4 (7.8%)
Followed too closely4 (7.8%)
Physical impairment2 (3.9%)
Exceeded authorized speed limit1 (2%)
Glare1 (2%)
Over-correcting/over-steering1 (2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions decreased proportionally from 83.3% in August 2022 to 74.5% in August 2023. Conversely, crashes under adverse weather conditions (cloudy or rainy) increased from 4 to 13. Crashes on wet road surfaces increased from 3 to 5, while those in daylight conditions saw a slight proportional decrease from 83.3% to 82.4%.

Weather

Clear38 (74.5%)
52.0%prior 25
Cloudy6 (11.8%)
Cloudy/Rain2 (3.9%)
Rain2 (3.9%)
Rain/Cloudy2 (3.9%)
Clear/Clear1 (2.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · Weather condition at time of crash

Lighting

Daylight42 (82.4%)
68.0%prior 25
Dark - lighted roadway7 (13.7%)
40.0%prior 5
Dawn1 (2.0%)
Dusk1 (2.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · Lighting condition field

Road Surface

Dry46 (90.2%)
76.9%prior 26
Wet5 (9.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · Road surface condition field

Vehicles & Demographics

The number of persons involved in crashes increased across most age groups, with the 26-34 age group seeing the largest increase from 8 to 23 persons. Toyota and Ford vehicles saw substantial increases in involvement, with Toyota rising from 4 to 14 and Ford from 1 to 14. Nissan vehicles also increased their involvement from 6 to 9.

Top Vehicle Makes (96 vehicles)

1
TOYOTA14 (14.6%)
2
FORD14 (14.6%)
3
CHEVROLET10 (10.4%)
4
NISSAN9 (9.4%)
50.0%prior 6
5
HYUNDAI6 (6.3%)
6
HONDA5 (5.2%)
-16.7%prior 6
7
SUBARU4 (4.2%)
8
VOLKSWAGEN3 (3.1%)
9
JEEP3 (3.1%)
-40.0%prior 5
10
BMW2 (2.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · Vehicle unit records

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

Sex Distribution (111 persons with recorded sex)

Male64 (57.7%)
68.4%prior 38
Female47 (42.3%)
123.8%prior 21

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 MPH speed zones increased from 20 to 25, while crashes in 55 MPH zones doubled from 7 to 14. There were no fatal crashes reported in any speed zone during either period. The current period also saw crashes reported in 5 MPH, 10 MPH, and 65 MPH zones, which were not present in the prior period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-08-01 to 2023-08-31 · 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: 2023-08-01 through 2023-08-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-08-01 through 2023-08-31 (31 days)
  • Geographic scope: WAKEFIELD, MA
  • Total crash records analyzed: 51
  • Total persons involved: 124
  • Total vehicles involved: 96

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: August 2023." Published June 21, 2026. Reporting period: 2023-08-01 to 2023-08-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/wakefield/august-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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Wakefield, MA Crash Report — August 2023 | ThatCarHitMe.com