Monthly Traffic Safety Analysis

84 CRASHES IN
WOBURN, MA
MAY 2023

All metrics benchmarked againstMay 2022

Total crashes in WOBURN increased by 37.7% from 61 in May 2022 to 84 in May 2023. While fatalities remained at zero in both periods, total injuries saw a substantial rise of 93.3%, increasing from 15 to 29. This indicates a significant increase in crash frequency and injury severity year-over-year.

84

37.7%was 61

Total Crash Events

0

Persons Killed

29

93.3%was 15

Persons Injured

8

60.0%was 5

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash activity in WOBURN increased year-over-year. Total crashes rose by 37.7%, from 61 in May 2022 to 84 in May 2023. This increase was accompanied by a 93.3% rise in total injuries, from 15 to 29.

8

Hit-and-Run Crashes — May 2023

60.0% vs prior (5)

Hit-and-run crashes increased by 60% in count, from 5 in May 2022 to 8 in May 2023. Consequently, the hit-and-run rate rose from 8.2% to 9.5% of all crashes, indicating an upward trend in these incidents.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

3

Pedestrians Injured

Prior: 1200.0%

26

Motorists Injured

Prior: 1485.7%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak hour for crashes remained 5p in both periods, with the count increasing from 9 in May 2022 to 11 in May 2023. The peak day for crashes shifted from Wednesday, with 14 crashes in May 2022, to Friday, with 15 crashes in May 2023.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at 0 in both May 2022 and May 2023. Total injuries increased significantly by 93.3%, rising from 15 to 29. While serious injuries remained constant at 1, minor injuries doubled from 5 to 11, and possible injuries also doubled from 6 to 12.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.2%
0.0%prior 1
Minor Injury11minor injury crashes13.1%
120.0%prior 5
Possible Injury12possible injury crashes14.3%
100.0%prior 6
No Injury56no injury crashes66.7%
21.7%prior 46

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, 'No improper driving' remained the most cited, increasing by 23.5% in count from 17 to 21. 'Failed to yield right of way' saw the largest percentage increase, rising by 125% in count from 4 to 9 crashes. 'Inattention' and 'Followed too closely' also increased in count, by 30% and 28.6% respectively, maintaining their positions among the top factors.

Officer-Reported Primary Contributing Cause

No improper driving21 (25%)23.5%prior 17
Inattention13 (15.5%)30.0%prior 10
Followed too closely9 (10.7%)28.6%prior 7
Failed to yield right of way9 (10.7%)
Failure to keep in proper lane or running off road5 (6%)
Made an improper turn3 (3.6%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (3.6%)
Disregarded traffic signs, signals, road markings2 (2.4%)
Distracted2 (2.4%)
Driving too fast for conditions1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather and dry road conditions. Crashes on wet road surfaces doubled from 3 in May 2022 to 6 in May 2023. Daylight crashes increased from 47 to 63, while crashes in dark-lighted roadway conditions slightly decreased from 12 to 10.

Weather

Clear72 (87.8%)
46.9%prior 49
Rain6 (7.3%)
Cloudy3 (3.7%)
-40.0%prior 5
Clear/Other1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Weather condition at time of crash

Lighting

Daylight63 (75.9%)
34.0%prior 47
Dark - lighted roadway10 (12.0%)
-16.7%prior 12
Dusk6 (7.2%)
Dark - roadway not lighted3 (3.6%)
Dawn1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Lighting condition field

Road Surface

Dry77 (92.8%)
35.1%prior 57
Wet6 (7.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 121 to 165 year-over-year. Toyota surpassed Honda as the top vehicle make involved, with its count rising from 17 to 28. The 26-34 age group continued to have the highest involvement, increasing from 26 to 44 persons, while the 16-20 age group saw a substantial increase from 9 to 25 persons.

Top Vehicle Makes (165 vehicles)

1
TOYOTA28 (17%)
64.7%prior 17
2
HONDA23 (13.9%)
27.8%prior 18
3
FORD17 (10.3%)
41.7%prior 12
4
NISSAN12 (7.3%)
0.0%prior 12
5
CHEVROLET9 (5.5%)
6
MERCEDES-BENZ8 (4.8%)
60.0%prior 5
7
HYUNDAI6 (3.6%)
8
JEEP6 (3.6%)
-40.0%prior 10
9
RAM5 (3%)
10
SUBARU4 (2.4%)
-20.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Vehicle unit records

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

Sex Distribution (184 persons with recorded sex)

Male112 (60.9%)
64.7%prior 68
Female72 (39.1%)
46.9%prior 49

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Person-level records linked to crash events

Speed Limit Zones

Fatal crashes remained at zero across all speed zones in both periods. The 30 mph zone consistently recorded the highest number of crashes, with 23 incidents in both May 2022 and May 2023. Notably, crashes in the 65 mph zone increased from 3 to 14, and the 35 mph zone also saw an increase from 15 to 20 crashes.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · 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: 2023-05-01 through 2023-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-05-01 through 2023-05-31 (31 days)
  • Geographic scope: WOBURN, MA
  • Total crash records analyzed: 84
  • Total persons involved: 210
  • Total vehicles involved: 165

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). "WOBURN, MA Crash Intelligence Report: May 2023." Published June 21, 2026. Reporting period: 2023-05-01 to 2023-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/woburn/may-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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Woburn, MA Crash Report — May 2023 | ThatCarHitMe.com