ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BURLINGTON, MA · MARCH 2023
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/march-2023-report
Monthly Traffic Safety Analysis
44 CRASHES IN
BURLINGTON, MA
MARCH 2023
In March 2023, Burlington experienced 44 crashes, a 57.1% increase compared to the 28 crashes recorded in March 2022. This period also saw a notable increase in crashes attributed to "followed too closely," which rose from 5 to 9 incidents.
44
▲ 57.1%was 28
Total Crash Events
0
Persons Killed
10
▲ 42.9%was 7
Persons Injured
1
▼ -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.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash incidents in Burlington showed an upward trend year-over-year, with total crashes increasing by 57.1% from 28 in March 2022 to 44 in March 2023. Concurrently, the total number of injuries rose from 7 to 10 during the same period.
1
Hit-and-Run Crashes — March 2023
▼ -66.7% vs prior (3)
Hit-and-run crashes decreased from 3 incidents in March 2022 to 1 incident in March 2023. Consequently, the hit-and-run rate saw a decline from 10.7% to 2.3% year-over-year.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
1
Pedestrians Injured
9
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak day for crashes remained Thursday in both periods, with 6 crashes in March 2022 and 9 crashes in March 2023. The peak hour for crashes shifted from 4 p.m. with 5 incidents in March 2022 to 2 p.m. with 6 incidents in March 2023. This indicates a general increase in crash frequency across similar temporal patterns.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatalities remained at 0 in both March 2022 and March 2023. Total injuries increased from 7 to 10 year-over-year. The proportion of minor injury crashes (severity B) decreased from 17.9% to 13.6%, while crashes with no injuries increased from 78.6% to 84.1% of all crashes.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Most severe injury per crash record
Top Contributing Factors
Crashes attributed to "followed too closely" increased from 5 incidents (17.9% share) in March 2022 to 9 incidents (20.5% share) in March 2023, marking an 80% increase in count. "Failed to yield right of way" also saw a 100% increase in count, rising from 3 crashes (10.7% share) to 6 crashes (13.6% share) year-over-year. "No improper driving" incidents increased from 6 to 9 crashes, representing a 50% increase in count, though its share slightly decreased from 21.4% to 20.5%. "Inattention" remained constant at 3 crashes in both periods.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in clear weather conditions increased from 16 in March 2022 to 27 in March 2023. Similarly, crashes on dry road surfaces rose from 18 to 34 during the same period. There was a decrease in crashes on icy roads from 4 to 0, and on snowy roads from 1 to 0.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 53 in March 2022 to 89 in March 2023. Toyota remained the most frequently involved make, increasing from 8 to 19 vehicles, while Ford saw a significant rise from 2 to 12 vehicles. Among persons involved, the 21-25 age group experienced a substantial increase from 5 to 19 individuals, and the 45-54 age group rose from 5 to 18 individuals.
Top Vehicle Makes (89 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Vehicle unit records
5 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (101 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes increased across several speed limit zones year-over-year, with the 55 mph zone experiencing the highest number of incidents in both periods, rising from 10 to 12 crashes. Crashes in the 30 mph zone doubled from 4 to 8, and in the 25 mph zone, they more than doubled from 2 to 5. No fatalities were recorded in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · 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: 2023-03-01 through 2023-03-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-03-01 through 2023-03-31 (31 days)
- Geographic scope: BURLINGTON, MA
- Total crash records analyzed: 44
- Total persons involved: 107
- Total vehicles involved: 89
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: March 2023." Published June 21, 2026. Reporting period: 2023-03-01 to 2023-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/burlington/march-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2023-03-01 – 2023-03-31
Generated: June 21, 2026 · All rights reserved