ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BURLINGTON, MA · SEPTEMBER 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/september-2023-report
Monthly Traffic Safety Analysis
38 CRASHES IN
BURLINGTON, MA
SEPTEMBER 2023
Total crashes in Burlington decreased by 15.6%, from 45 in September 2022 to 38 in September 2023. Despite this overall reduction, the number of injuries increased significantly by 64.3%, rising from 14 to 23. A notable shift was also observed in DUI-related crashes, which increased from 0 in the prior period to 2 in the current period.
38
▼ -15.6%was 45
Total Crash Events
0
Persons Killed
23
▲ 64.3%was 14
Persons Injured
3
▲ 200.0%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. 1 crash with unreported severity is not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend for crashes in Burlington shows a decrease, with total crashes falling by 15.6% year-over-year, from 45 in September 2022 to 38 in September 2023. Despite this reduction in crash volume, the number of total injuries increased by 64.3%, rising from 14 to 23.
3
Hit-and-Run Crashes — September 2023
▲ 200.0% vs prior (1)
Hit-and-run crashes increased significantly year-over-year, rising from 1 crash in September 2022 to 3 crashes in September 2023. This resulted in the hit-and-run rate more than tripling, from 2.2% of total crashes in the prior period to 7.9% in the current period.
Vulnerable Road User Casualties
0
Motorists Killed
23
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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 Monday in September 2022, with 11 crashes, to Saturday in September 2023, with 8 crashes. Similarly, the peak hour for crashes moved from 6 PM (7 crashes) in the prior period to 4 PM (6 crashes) in the current period. This indicates a shift in crash occurrence towards weekends and earlier afternoon hours.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Neither period recorded any fatalities or fatal crashes. While total crashes decreased, the number of total injuries increased from 14 in September 2022 to 23 in September 2023, a 64.3% rise. Serious injuries, coded as 'A', were reported in 1 crash (2.6% share) in the current period but not in the prior period, while minor injuries ('B') increased from 10 crashes (22.2% share) to 11 crashes (28.9% share).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor shifted from "Followed too closely" in September 2022 (17 crashes) to "Failed to yield right of way" in September 2023 (9 crashes). Crashes attributed to "Followed too closely" saw a significant decrease from 17 to 5, while "Failed to yield right of way" crashes increased from 1 to 9. "Inattention" crashes saw a slight increase from 6 to 7, and "No improper driving" remained stable at 4 crashes in both periods.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in clear weather conditions decreased from 35 (77.8% of crashes) in September 2022 to 21 (55.3% of crashes) in September 2023. Conversely, crashes under adverse weather conditions (rain, cloudy, or mixed) increased from 8 (17.8% of crashes) to 15 (39.5% of crashes). The proportion of crashes on wet road surfaces also increased, from 7 (15.6% of crashes) to 11 (28.9% of crashes).
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased from 92 in September 2022 to 72 in September 2023. While TOYOTA and HONDA remained among the top makes, their counts decreased from 13 each to 8 each. The age group 26-34 saw a decrease in persons involved from 21 to 14, while the 0-15 age group increased from 2 to 7. The number of females involved in crashes decreased from 46 to 31, while males increased from 47 to 52.
Top Vehicle Makes (72 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records
6 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (83 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes in the 55 mph speed zone significantly decreased from 23 in September 2022 to 6 in September 2023. Conversely, crashes in the 35 mph speed zone more than doubled, increasing from 4 to 12. Crashes in the 30 mph zone also rose from 5 to 9, indicating a shift of crash occurrences to lower speed limit areas.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-09-01 through 2023-09-30 (30 days)
- Geographic scope: BURLINGTON, MA
- Total crash records analyzed: 38
- Total persons involved: 89
- Total vehicles involved: 72
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: September 2023." Published June 21, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/burlington/september-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: 2023-09-01 – 2023-09-30
Generated: June 21, 2026 · All rights reserved