Monthly Traffic Safety Analysis

87 CRASHES IN
WOBURN, MA
MARCH 2022

All metrics benchmarked againstMarch 2021

Total crashes in WOBURN increased from 67 in March 2021 to 87 in March 2022, representing a 29.85% rise. The most notable year-over-year shift was a significant increase in hit-and-run incidents.

87

29.9%was 67

Total Crash Events

0

Persons Killed

27

22.7%was 22

Persons Injured

6

200.0%was 2

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in WOBURN are trending upwards year-over-year, with a 29.85% increase in total crashes from March 2021 to March 2022. This indicates a notable rise in crash occurrences during the current period.

6

Hit-and-Run Crashes — March 2022

200.0% vs prior (2)

Hit-and-run crashes increased from 2 in March 2021 to 6 in March 2022. Concurrently, the hit-and-run rate rose from 3.0% to 6.9% 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: 0%

24

Motorists Injured

Prior: 2114.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Wednesday with 13 crashes in March 2021 to Monday with 25 crashes in March 2022. Similarly, the peak crash hour moved from 3 PM with 9 crashes in March 2021 to 8 AM with 12 crashes in March 2022, indicating a change in temporal patterns.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatalities reported in either March 2021 or March 2022. Total injuries increased from 22 in March 2021 to 27 in March 2022. While March 2021 recorded 1 serious injury, March 2022 reported none, with minor and possible injury proportions seeing slight decreases in share of total crashes.

Outcome by Severity (Crash Events)

Minor Injury10minor injury crashes11.5%
11.1%prior 9
Possible Injury8possible injury crashes9.2%
0.0%prior 8
No Injury63no injury crashes72.4%
34.0%prior 47

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' increased by 8, from 15 in March 2021 to 23 in March 2022. 'Inattention' factors saw an increase of 6 crashes, from 5 to 11, while 'Followed too closely' increased by 2 crashes, from 12 to 14. Conversely, 'Failed to yield right of way' decreased by 3 crashes, from 7 to 4, and 'Over-correcting/over-steering' decreased by 3 crashes, from 4 to 1.

Officer-Reported Primary Contributing Cause

No improper driving23 (26.4%)53.3%prior 15
Followed too closely14 (16.1%)16.7%prior 12
Inattention11 (12.6%)120.0%prior 5
Other improper action5 (5.7%)0.0%prior 5
Failure to keep in proper lane or running off road4 (4.6%)
Failed to yield right of way4 (4.6%)-42.9%prior 7
Distracted3 (3.4%)
Driving too fast for conditions3 (3.4%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (2.3%)
Fatigued/asleep2 (2.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased from 52 to 64, while those in 'Cloudy' conditions rose from 2 to 8. The number of crashes on 'Dry' road surfaces increased from 60 to 67, and on 'Wet' surfaces from 6 to 11. Notably, 7 crashes occurred on 'Ice' and 5 crashes occurred in 'Snow' conditions in March 2022, which were not prominent in March 2021 data.

Weather

Clear64 (76.2%)
23.1%prior 52
Cloudy8 (9.5%)
Cloudy/Rain2 (2.4%)
Cloudy/Snow2 (2.4%)
Rain2 (2.4%)
Clear/Other2 (2.4%)
Snow2 (2.4%)
Snow/Other1 (1.2%)
Sleet, hail (freezing rain or drizzle)/Snow1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Weather condition at time of crash

Lighting

Daylight66 (77.6%)
34.7%prior 49
Dark - lighted roadway15 (17.6%)
66.7%prior 9
Dark - roadway not lighted2 (2.4%)
-60.0%prior 5
Dawn1 (1.2%)
Dusk1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Lighting condition field

Road Surface

Dry67 (78.8%)
11.7%prior 60
Wet11 (12.9%)
83.3%prior 6
Ice7 (8.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 131 to 170 year-over-year. Toyota remained the top make, increasing from 19 to 31 vehicles, while Honda maintained 19 vehicles in both periods. The 26-34 age group saw the largest increase in persons involved, rising from 32 to 44.

Top Vehicle Makes (170 vehicles)

1
TOYOTA31 (18.2%)
63.2%prior 19
2
HONDA19 (11.2%)
0.0%prior 19
3
FORD19 (11.2%)
18.8%prior 16
4
NISSAN14 (8.2%)
16.7%prior 12
5
JEEP11 (6.5%)
120.0%prior 5
6
CHEVROLET10 (5.9%)
-28.6%prior 14
7
SUBARU7 (4.1%)
16.7%prior 6
8
MAZDA5 (2.9%)
9
GMC5 (2.9%)
10
HYUNDAI5 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Vehicle unit records

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

Sex Distribution (171 persons with recorded sex)

Male98 (57.3%)
24.1%prior 79
Female73 (42.7%)
35.2%prior 54

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 35 mph speed zones increased from 10 to 17, and in 55 mph zones from 10 to 14. The 65 mph speed zone saw an increase from 7 to 16 crashes year-over-year. There were no fatal crashes reported across any speed zones in either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · 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: 2022-03-01 through 2022-03-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-03-01 through 2022-03-31 (31 days)
  • Geographic scope: WOBURN, MA
  • Total crash records analyzed: 87
  • Total persons involved: 190
  • Total vehicles involved: 170

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: March 2022." Published June 21, 2026. Reporting period: 2022-03-01 to 2022-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/woburn/march-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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Woburn, MA Crash Report — March 2022 | ThatCarHitMe.com