ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BURLINGTON, MA · OCTOBER 2022
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/burlington/october-2022-report
Monthly Traffic Safety Analysis
58 CRASHES IN
BURLINGTON, MA
OCTOBER 2022
Total crashes in Burlington for October increased by 28.9%, from 45 in 2021 to 58 in 2022. While total fatalities decreased from 1 to 0, the number of injured persons rose significantly from 9 to 17. The most notable year-over-year shift was the 88.9% increase in total injuries.
58
▲ 28.9%was 45
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
17
▲ 88.9%was 9
Persons Injured
5
▲ 66.7%was 3
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. 1 crash with unreported severity is not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash activity in Burlington shows an upward trend year-over-year, with total crashes increasing by 28.9% from 45 in the prior period to 58 in the current period. Total injuries also increased substantially by 88.9%, from 9 to 17. However, total fatalities decreased from 1 to 0 during this period.
5
Hit-and-Run Crashes — October 2022
▲ 66.7% vs prior (3)
The number of hit-and-run crashes increased from 3 in the prior period to 5 in the current period. Consequently, the hit-and-run crash rate also increased from 6.7% of total crashes to 8.6% of total crashes year-over-year. This indicates an upward trend in both the count and proportion of hit-and-run incidents.
Vulnerable Road User Casualties
0
Motorists Killed
17
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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, with both Thursday and Sunday recording 10 crashes in the current period, compared to Sunday having 9 crashes in the prior period. The peak hour for crashes also shifted from 6 PM with 6 crashes in the prior period to 5 PM with 8 crashes in the current period. Crashes occurring on Mondays decreased from 7 to 6, while Friday crashes increased from 5 to 8.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes decreased from 1 in the prior period to 0 in the current period. The number of serious injury crashes remained constant at 1 in both periods. Minor injury crashes increased from 4 to 7, and possible injury crashes increased from 4 to 6. The proportion of crashes resulting in any injury (serious, minor, or possible) increased from 22.2% in the prior period to 24.1% in the current period.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Most severe injury per crash record
Top Contributing Factors
The contributing factor 'Followed too closely' saw a significant increase in count, rising from 5 crashes in the prior period to 14 crashes in the current period, becoming the top factor. Crashes attributed to 'No improper driving' increased from 8 to 10, and 'Failed to yield right of way' increased from 5 to 6. Conversely, 'Driving too fast for conditions' decreased from 5 crashes to 4, and 'Inattention' decreased from 5 crashes to 2.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring under 'Clear' weather conditions increased from 28 in the prior period to 40 in the current period, while 'Rain' conditions remained stable at 8 crashes. The number of crashes during 'Daylight' hours increased from 28 to 34, and those in 'Dark - lighted roadway' increased from 10 to 16. Crashes on 'Dry' road surfaces increased from 31 to 41, and on 'Wet' surfaces from 12 to 16.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 85 in the prior period to 117 in the current period. Toyota remained the most frequently involved make, increasing from 11 vehicles to 21. Honda also saw an increase from 10 to 16 vehicles, while Ford significantly increased its involvement from 3 vehicles to 16, tying with Honda in the current period.
Top Vehicle Makes (117 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Vehicle unit records
6 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (132 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Person-level records linked to crash events
Speed Limit Zones
The 55 mph speed zone continued to have the highest number of crashes, increasing from 15 in the prior period to 18 in the current period. Crashes in the 30 mph zone more than doubled from 7 to 15, and in the 35 mph zone increased from 10 to 14. Notably, the prior period recorded one fatal crash in a 15 mph speed zone, whereas no fatal crashes were reported in any speed zone in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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: 2022-10-01 through 2022-10-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-10-01 through 2022-10-31 (31 days)
- Geographic scope: BURLINGTON, MA
- Total crash records analyzed: 58
- Total persons involved: 141
- Total vehicles involved: 117
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: October 2022." Published June 21, 2026. Reporting period: 2022-10-01 to 2022-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/burlington/october-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2022-10-01 – 2022-10-31
Generated: June 21, 2026 · All rights reserved