ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BURLINGTON, MA · 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/2022-annual-report
Yearly Traffic Safety Analysis
479 CRASHES IN
BURLINGTON, MA
2022
In 2022, Burlington recorded 479 total vehicle crashes, a decrease from 522 crashes in 2021, representing an 8.2% year-over-year reduction. Despite the drop in overall incidents, the number of fatalities doubled from one to two. The most notable shift was in driver behavior, where crashes attributed to 'Followed too closely' increased by 35.3%, becoming the leading contributing factor in 2022 after being ranked second in the prior year.
479
▼ -8.2%was 522
Total Crash Events
2
▲ 100.0%was 1
Persons Killed
142
▲ 3.6%was 137
Persons Injured
22
▼ -15.4%was 26
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 4 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in crash volume shows a decrease, with total incidents falling by 8.2% from 522 in 2021 to 479 in 2022. However, the severity of these crashes saw a slight increase, as total injuries rose by 3.6% from 137 to 142, and total fatalities increased from one to two.
22
Hit-and-Run Crashes — 2022
▼ -15.4% vs prior (26)
Hit-and-run incidents trended downward from 2021 to 2022. The total count of hit-and-run crashes decreased from 26 to 22. Correspondingly, the hit-and-run rate as a percentage of total crashes also fell, from 5.0% in 2021 to 4.6% in 2022.
Vulnerable Road User Casualties
0
Pedestrians Killed
2
Motorists Killed
0
Other Killed
7
Pedestrians Injured
134
Motorists Injured
1
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of crashes shifted between the two periods. In 2022, the peak day for crashes was Monday with 80 incidents, and the peak hour was 5 p.m. with 63 incidents. This contrasts with 2021, when the peak day was Thursday (95 crashes) and the peak hour was earlier, at 3 p.m. (52 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes decreased, the fatal crash rate more than doubled, rising from 0.19% in 2021 to 0.42% in 2022. The number of fatal crashes increased from one to two. The proportion of crashes resulting in any injury (Fatal, Serious, Minor, or Possible) was slightly higher in 2022 (25.0%) compared to 2021 (22.4%), though the share of crashes classified as 'Serious Injury' decreased from 2.5% to 1.0%.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
The ranking of top contributing factors changed year-over-year. In 2022, 'Followed too closely' was the leading factor, cited in 115 crashes, a 35.3% increase in count from 85 crashes in 2021 when it was the second-ranked factor. Conversely, crashes with 'No improper driving' cited decreased by 19.6% from 102 to 82, moving from the top factor in 2021 to the second-ranked in 2022. Incidents involving 'Inattention' also saw a small decrease in count from 59 to 53.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions remained largely consistent between 2021 and 2022. In both years, the majority of crashes occurred in 'Clear' weather (64.9% in 2021, 68.3% in 2022) and on 'Dry' road surfaces (78.7% in 2021, 78.5% in 2022). There was a minor shift in lighting conditions, with the share of crashes in 'Daylight' increasing from 64.4% to 68.7%, while the share in 'Dark - lighted roadway' conditions decreased from 24.7% to 20.9%.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes remained consistent in both rank and general volume: Toyota (165 in 2021, 158 in 2022), Honda (152 in 2021, 147 in 2022), and Ford (93 in both years). The age demographics of persons involved in crashes also showed stability, with the 26-34 age group being the most represented in both 2021 (234 persons) and 2022 (236 persons).
Top Vehicle Makes (966 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
45 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,063 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones saw some changes. Crashes in 55 mph zones increased from 158 in 2021 to 186 in 2022, while incidents in 35 mph zones decreased from 138 to 118. The location of fatal crashes also shifted; the single fatal crash in 2021 occurred in a 15 mph zone, whereas the two fatal crashes in 2022 happened in 35 mph and 40 mph zones.
Fatal crashes by zone: 35 mph: 1 of 118 (0.847%) · 40 mph: 1 of 9 (11.111%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: BURLINGTON, MA
- Total crash records analyzed: 479
- Total persons involved: 1,167
- Total vehicles involved: 966
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: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/burlington/2022-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: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved