ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BURLINGTON, MA · 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/2023-annual-report
Yearly Traffic Safety Analysis
598 CRASHES IN
BURLINGTON, MA
2023
In 2023, Burlington recorded 598 total vehicle crashes, a 24.8% increase from the 479 crashes reported in 2022. While total fatalities decreased from two to one year-over-year, the number of persons injured rose from 142 to 191. The most notable shift was the overall increase in crash volume, with contributing factors like 'Inattention' and 'Failed to yield right of way' seeing significant growth in incident counts.
598
▲ 24.8%was 479
Total Crash Events
1
▼ -50.0%was 2
Persons Killed
191
▲ 34.5%was 142
Persons Injured
20
▼ -9.1%was 22
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. 3 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash data for Burlington indicates a rising trend in 2023 compared to the prior year. Total crashes increased by 24.8%, from 479 in 2022 to 598 in 2023. This was accompanied by a 34.5% increase in total injuries, from 142 to 191, although fatalities decreased from two to one.
20
Hit-and-Run Crashes — 2023
▼ -9.1% vs prior (22)
The number of hit-and-run incidents decreased from 22 in 2022 to 20 in 2023. The hit-and-run rate, measured as the number of such incidents per 100 crashes, also trended downward. The rate fell from 4.6 in the prior year to 3.3 in the current year, indicating that hit-and-runs constituted a smaller proportion of total crashes in 2023.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
3
Pedestrians Injured
4
Cyclists Injured
184
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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 between 2022 and 2023. The peak day for crashes moved from Monday (80 crashes) in 2022 to Wednesday (99 crashes) in 2023. The peak hour for collisions also shifted slightly earlier, from 5 p.m. (63 crashes) in the prior year to 4 p.m. (53 crashes) in the current year. Both periods show a concentration of crashes during the afternoon commute hours.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes saw a mixed change year-over-year. The number of fatal crashes decreased from two in 2022 to one in 2023, and the fatal crash rate per 100 crashes dropped from 0.42 to 0.17. The proportion of crashes resulting in any injury remained relatively stable, accounting for 24.6% of crashes in 2022 and 23.9% in 2023. Consequently, crashes resulting in no injury made up a similar share of the total in both years (74.1% in 2022 and 75.4% in 2023).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The primary contributing factors to crashes in Burlington showed some notable shifts between 2022 and 2023. 'Followed too closely' remained the top cited factor in both years, though its count decreased from 115 to 109. Crashes attributed to 'Inattention' increased by 43.4%, from 53 incidents in 2022 to 76 in 2023. The count of crashes involving 'Failed to yield right of way' grew from 33 to 65, and 'Failure to keep in proper lane or running off road' increased from 11 to 34 incidents.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions remained broadly similar year-over-year, with most incidents in both 2022 and 2023 occurring in daylight on dry roads. In 2023, 63.4% of crashes happened in daylight, down from a 68.7% share in 2022, while crashes on dark but lighted roadways increased their share from 20.9% to 23.9%. The proportion of crashes on wet roads increased from 14.4% to 17.4%, while collisions on roads with snow or ice decreased, accounting for 3.2% of crashes in 2023 compared to 6.7% in 2022.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes—Toyota, Honda, and Ford—remained consistent year-over-year, with each showing an increase in total count. In 2023, Toyotas were involved in 223 crashes, up from 158 in 2022. The age demographics of persons involved in crashes showed some proportional shifts. The share of individuals aged 65 and older increased from 8.8% of persons in 2022 to 10.1% in 2023, while the proportion of those aged 16-20 decreased from 10.5% to 9.3%.
Top Vehicle Makes (1,185 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
60 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,343 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones changed between the two periods. In 2023, there was a notable increase in crashes within 30 mph zones (from 83 to 131 incidents) and 35 mph zones (from 118 to 180 incidents). Crashes in 55 mph zones remained high but saw a slight decrease from 186 to 175 incidents. The location of fatal crashes also shifted; in 2022, two fatal crashes occurred in 35 mph and 40 mph zones, while in 2023, the single fatal crash occurred in a 30 mph zone.
Fatal crashes by zone: 30 mph: 1 of 131 (0.763%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: BURLINGTON, MA
- Total crash records analyzed: 598
- Total persons involved: 1,437
- Total vehicles involved: 1,185
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: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/burlington/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved