Monthly Traffic Safety Analysis

45 CRASHES IN
BURLINGTON, MA
JULY 2023

All metrics benchmarked againstJuly 2022

In Burlington, total crashes increased by 21.62% year-over-year, rising from 37 in July 2022 to 45 in July 2023. This period also saw a 23.53% increase in total injuries, from 17 to 21. The most notable shift was the overall increase in crash incidents and associated injuries.

45

21.6%was 37

Total Crash Events

0

Persons Killed

21

23.5%was 17

Persons Injured

2

100.0%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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates an increase in crash incidents, with total crashes rising from 37 in July 2022 to 45 in July 2023. This represents a 21.62% increase in crashes year-over-year. Total injuries also increased by 23.53%, from 17 to 21.

2

Hit-and-Run Crashes — July 2023

100.0% vs prior (1)

The number of hit-and-run crashes increased from 1 in July 2022 to 2 in July 2023. This resulted in an increase in the hit-and-run rate from 2.7% to 4.4%. The hit-and-run rate is trending upward year-over-year.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

21

Motorists Injured

Prior: 1723.5%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Wednesday with 10 incidents in July 2022 to Saturday with 8 incidents in July 2023. The peak hour for crashes remained concentrated in the late afternoon to early evening, with 7 crashes at 5p in the prior period and 7 crashes at 6p in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes in either period. The current period saw no serious injuries (Severity A), compared to 1 serious injury in the prior period. Minor injury crashes decreased in proportion from 27% to 22.2%, while possible injury crashes increased from 5.4% to 11.1% of total crashes.

Outcome by Severity (Crash Events)

Minor Injury10minor injury crashes22.2%
0.0%prior 10
Possible Injury5possible injury crashes11.1%
150.0%prior 2
No Injury30no injury crashes66.7%
25.0%prior 24

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Most severe injury per crash record

Top Contributing Factors

The number of crashes attributed to 'Followed too closely' increased from 7 in July 2022 to 9 in July 2023, a 28.57% increase in count. Conversely, crashes with 'No improper driving' as a factor decreased from 7 to 4, a 42.86% decrease in count. 'Failed to yield right of way' also saw an increase in count from 4 to 5 crashes.

Officer-Reported Primary Contributing Cause

Followed too closely9 (20%)28.6%prior 7
Failed to yield right of way5 (11.1%)
No improper driving4 (8.9%)-42.9%prior 7
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner4 (8.9%)
Inattention3 (6.7%)
Failure to keep in proper lane or running off road2 (4.4%)
Driving too fast for conditions2 (4.4%)
Other improper action2 (4.4%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (4.4%)
Over-correcting/over-steering1 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased from 32 to 36 year-over-year. Crashes on 'Wet' road surfaces increased from 1 in the prior period to 5 in the current period. 'Daylight' remained the dominant lighting condition for crashes, with 36 in the prior period and 37 in the current period.

Weather

Clear36 (80.0%)
12.5%prior 32
Cloudy4 (8.9%)
Cloudy/Rain2 (4.4%)
Clear/Unknown1 (2.2%)
Rain1 (2.2%)
Rain/Cloudy1 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Weather condition at time of crash

Lighting

Daylight37 (82.2%)
2.8%prior 36
Dark - lighted roadway6 (13.3%)
Dark - roadway not lighted1 (2.2%)
Dusk1 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Lighting condition field

Road Surface

Dry40 (88.9%)
14.3%prior 35
Wet5 (11.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Road surface condition field

Vehicles & Demographics

The number of Toyota vehicles involved in crashes doubled from 10 in July 2022 to 20 in July 2023, making it the top make. Honda vehicles involved in crashes decreased from 12 to 7. The 55-64 age group saw a significant increase in persons involved in crashes, from 10 to 25, while the 65+ age group decreased from 14 to 8.

Top Vehicle Makes (93 vehicles)

1
TOYOTA20 (21.5%)
100.0%prior 10
2
FORD11 (11.8%)
83.3%prior 6
3
CHEVROLET10 (10.8%)
66.7%prior 6
4
NISSAN10 (10.8%)
5
HONDA7 (7.5%)
-41.7%prior 12
6
SUBARU5 (5.4%)
7
MERCEDES-BENZ4 (4.3%)
8
ACURA3 (3.2%)
9
CADI3 (3.2%)
10
VOLVO3 (3.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Vehicle unit records

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

Sex Distribution (118 persons with recorded sex)

Male63 (53.4%)
28.6%prior 49
Female55 (46.6%)
57.1%prior 35

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 35 mph zones remained the most frequent, increasing slightly from 15 in the prior period to 16 in the current period. Crashes in 30 mph zones notably increased from 5 to 11. No fatal crashes were recorded in any speed zone for either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · 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: 2023-07-01 through 2023-07-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-07-01 through 2023-07-31 (31 days)
  • Geographic scope: BURLINGTON, MA
  • Total crash records analyzed: 45
  • Total persons involved: 123
  • Total vehicles involved: 93

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). "BURLINGTON, MA Crash Intelligence Report: July 2023." Published June 21, 2026. Reporting period: 2023-07-01 to 2023-07-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/burlington/july-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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Burlington, MA Crash Report — July 2023 | ThatCarHitMe.com