ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · AUBURN, MA · JUNE 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/auburn/june-2024-report
Monthly Traffic Safety Analysis
84 CRASHES IN
AUBURN, MA
JUNE 2024
In June 2024, Auburn experienced 84 crashes, a slight increase of 1.2% compared to the 83 crashes reported in June 2023. Total injuries saw a notable increase of 40%, rising from 25 to 35. This period also saw the first serious injury crash reported, along with a significant 200% increase in DUI-related crashes.
84
▲ 1.2%was 83
Total Crash Events
0
Persons Killed
35
▲ 40.0%was 25
Persons Injured
5
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 · 2024-06-01 to 2024-06-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crashes in Auburn remained relatively stable year-over-year, with a minor increase from 83 to 84 total crashes. However, total injuries increased by 40%, from 25 in June 2023 to 35 in June 2024, indicating a rise in crash severity. Fatalities remained at zero for both periods.
5
Hit-and-Run Crashes — June 2024
▼ 0.0% vs prior (5)
The number of hit-and-run crashes remained stable at 5 for both June 2023 and June 2024. Consequently, the hit-and-run rate also remained unchanged at 6% of total crashes for both periods.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
1
Pedestrians Injured
34
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal pattern of crashes shifted notably year-over-year. The peak day for crashes moved from Saturday (22 crashes) in June 2023 to Wednesday (18 crashes) in June 2024. The peak hour also changed, moving from 5 p.m. (10 crashes) in June 2023 to 2 p.m. (13 crashes) in June 2024, with Saturday crashes decreasing by 10 and Wednesday crashes increasing by 8.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes remained at zero for both periods, with no change in the fatal crash rate. However, there was a significant shift in injury severity, with one serious injury crash occurring in June 2024 compared to none in June 2023. Minor injury crashes increased from 10 to 19, and the proportion of all crashes resulting in any injury rose from 19.3% in June 2023 to 31% in June 2024.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Most severe injury per crash record
Top Contributing Factors
The top contributing factors saw significant shifts in counts and rankings year-over-year. 'Followed too closely' crashes decreased by 17, from 28 to 11, moving from the most frequent factor to fourth. Conversely, 'No improper driving' crashes increased by 9, from 9 to 18, becoming the most frequent factor. 'Inattention' crashes increased by 5, from 9 to 14, while 'Failed to yield right of way' remained stable at 14 crashes.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in clear weather conditions increased from 46 to 63, while those in cloudy conditions decreased from 20 to 7. Similarly, crashes on dry road surfaces increased from 70 to 76, and those on wet surfaces decreased from 13 to 6. Crashes occurring in daylight conditions increased from 71 to 76, while those in dark-lighted roadway conditions decreased from 5 to 1.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased slightly from 163 to 158. Toyota remained a top make, though its involvement in crashes decreased from 26 to 18 vehicles. Honda and Nissan saw increased involvement, with Honda rising from 12 to 17 vehicles and Nissan from 6 to 14 vehicles. The age group 26-34 saw the largest decrease in persons involved (from 48 to 34), while the 45-54 age group experienced the largest increase (from 14 to 22).
Top Vehicle Makes (158 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Vehicle unit records
7 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (168 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Person-level records linked to crash events
Speed Limit Zones
There was a notable shift in the distribution of crashes across speed zones year-over-year. Crashes in the 65 mph speed zone decreased significantly from 37 to 24, while crashes in the 30 mph zone increased substantially from 8 to 23. No fatal crashes were recorded in any speed zone for either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · 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: 2024-06-01 through 2024-06-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-06-01 through 2024-06-30 (30 days)
- Geographic scope: AUBURN, MA
- Total crash records analyzed: 84
- Total persons involved: 180
- Total vehicles involved: 158
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). "AUBURN, MA Crash Intelligence Report: June 2024." Published June 21, 2026. Reporting period: 2024-06-01 to 2024-06-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/auburn/june-2024-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: 2024-06-01 – 2024-06-30
Generated: June 21, 2026 · All rights reserved