ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BURLINGTON, MA · 2024
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/2024-annual-report
Yearly Traffic Safety Analysis
632 CRASHES IN
BURLINGTON, MA
2024
In 2024, Burlington recorded 632 total crashes, a 5.7% increase from the 598 crashes reported in 2023. While total fatalities remained stable with one death in each period, the most notable shift was a significant increase in crash severity, as the number of collisions resulting in serious injuries more than doubled from 7 in 2023 to 15 in 2024.
632
▲ 5.7%was 598
Total Crash Events
1
Persons Killed
193
▲ 1.0%was 191
Persons Injured
24
▲ 20.0%was 20
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 11 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash data for Burlington shows an upward trend, with total collisions increasing by 5.7% from 598 in 2023 to 632 in 2024. The number of people injured remained nearly stable, rising from 191 to 193 year-over-year. Fatalities held constant at one death recorded in both the current and prior periods.
24
Hit-and-Run Crashes — 2024
▲ 20.0% vs prior (20)
The incidence of hit-and-run crashes trended upward year-over-year. The total count of hit-and-run events increased from 20 in 2023 to 24 in 2024. This corresponds to a rise in the hit-and-run rate, which grew from 3.3% of all crashes in the prior period to 3.8% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
2
Pedestrians Injured
1
Cyclists Injured
190
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted slightly between the two periods. The peak day for collisions moved from Wednesday (99 crashes) in 2023 to Thursday (104 crashes) in 2024. A similar change occurred with the peak time, which shifted from the 4 PM hour in the prior year (53 crashes) to the 5 PM hour in the current year (63 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While the number of fatal crashes was unchanged at one in both 2023 and 2024, the severity of non-fatal injury crashes intensified. The count of crashes classified as 'Serious Injury' more than doubled, rising from 7 to 15, and their share of all crashes increased from 1.2% to 2.4%. Consequently, the proportion of crashes resulting in 'No Injury' decreased from 75.4% in 2023 to 73.7% in 2024.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record
Top Contributing Factors
The top contributing factor in both periods was 'Followed too closely,' with the count of such crashes increasing by 21.1% from 109 in 2023 to 132 in 2024. 'Failed to yield right of way' became the third most common factor in 2024 with 77 incidents, up from 65 the prior year. Notably, crashes attributed to 'Inattention' decreased in count by 27.6% (from 76 to 55), while those linked to 'Exceeded authorized speed limit' saw a 75% increase in count (from 8 to 14).
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in 2024 were more likely to occur under favorable conditions compared to the prior year. Collisions on dry road surfaces accounted for 80.7% of the total (510 crashes), an increase from a 78.6% share (470 crashes) in 2023. Correspondingly, crashes on non-dry surfaces like wet or snow-covered roads decreased from 126 to 115. The share of crashes occurring in daylight also grew, rising from 63.4% in 2023 to 66.8% in 2024.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes were Toyota, Honda, and Ford in both years. However, the number of Hondas involved in collisions increased from 163 to 192, while Toyotas saw a slight decrease from 223 to 215. Among persons involved, the 26-34 age group remained the largest demographic, with its count growing from 272 to 303. The 45-54 age group also saw increased representation, moving into the top three most-involved age brackets in 2024.
Top Vehicle Makes (1,254 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
73 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,359 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
A notable shift occurred in the distribution of crashes by speed zone. In 2024, the 55 mph zone became the most frequent location for crashes with 207 incidents, up from 175 the prior year. Conversely, crashes in the 35 mph zone, which was the top location in 2023 with 180 crashes, decreased to 138. The single fatal crash in 2023 occurred in a 30 mph zone, whereas the fatal crash in 2024 was not attributed to a specific speed zone in the data.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: BURLINGTON, MA
- Total crash records analyzed: 632
- Total persons involved: 1,477
- Total vehicles involved: 1,254
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: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/burlington/2024-annual-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: 2024-01-01 – 2024-12-31
Generated: June 21, 2026 · All rights reserved