ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MEDFORD, MA · JANUARY 2026
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/medford/january-2026-report
Monthly Traffic Safety Analysis
121 CRASHES IN
MEDFORD, MA
JANUARY 2026
Total crashes in MEDFORD for January 2026 increased to 121, a 44.05% rise from 84 crashes in January 2025. Fatalities remained at zero in both periods, while total injuries increased by 35.29%. The most notable shift was a significant increase in crashes attributed to 'Driving too fast for conditions', which rose from 1 to 5 incidents.
121
▲ 44.0%was 84
Total Crash Events
0
Persons Killed
23
▲ 35.3%was 17
Persons Injured
20
▼ -9.1%was 22
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. 15 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a significant increase in crash activity year-over-year, with total crashes rising by 44.05% from 84 to 121. Concurrently, total injuries also saw a substantial increase of 35.29%, from 17 to 23. Fatalities remained unchanged at zero for both periods.
20
Hit-and-Run Crashes — January 2026
▼ -9.1% vs prior (22)
The number of hit-and-run crashes decreased from 22 in January 2025 to 20 in January 2026. Consequently, the hit-and-run rate experienced a downward trend, decreasing from 26.2% to 16.5% of all crashes.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
1
Pedestrians Injured
1
Cyclists Injured
21
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · 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 Friday with 22 incidents in January 2025 to Thursday with 29 incidents in January 2026. While the peak hour remained 3p in both periods, the number of crashes at this hour increased from 8 to 14. Additionally, crashes on Monday significantly increased from 17 to 28.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes remained at zero in both periods, indicating no change in the fatal crash rate. The number of serious injuries (severity A) remained constant at 1, though its proportion of total crashes decreased from 1.2% to 0.8%. Minor injuries (severity B) increased from 7 to 10, and possible injuries (severity C) rose from 6 to 8.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Most severe injury per crash record
Top Contributing Factors
Among contributing factors, 'No improper driving' increased by 9 crashes (37.5%), rising from 24 to 33 incidents. 'Driving too fast for conditions' saw a substantial increase of 4 crashes (400%), growing from 1 to 5 incidents. 'Inattention' also increased by 3 crashes (50%), moving from 6 to 9 incidents.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
There was a notable shift in road surface conditions, with crashes on wet surfaces increasing from 7 to 27, and those on snowy surfaces rising from 11 to 26. Conversely, crashes on dry road surfaces decreased from 65 to 58. Crashes during clear weather conditions (Clear and Clear/Clear combined) increased from 63 to 74, while those in snow-related conditions (various snow types combined) increased from 11 to 23.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 162 to 235 year-over-year. Toyota moved from the third most common make (17 vehicles) to the first (46 vehicles), while Honda shifted from first (24 vehicles) to second (30 vehicles). In terms of demographics, the 35-44 age group became the most represented in crashes, increasing from 20 to 56 persons, and male involvement rose from 78 to 141 persons.
Top Vehicle Makes (235 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Vehicle unit records
54 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (229 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph speed zones increased from 63 to 87, remaining the most frequent speed limit for crashes in both periods. There was also an increase in crashes at 10 mph zones, from 2 to 6, and at 55 mph zones, from 4 to 8. Conversely, crashes in 35 mph zones slightly decreased from 9 to 7.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · 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: 2026-01-01 through 2026-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2026-01-01 through 2026-01-31 (31 days)
- Geographic scope: MEDFORD, MA
- Total crash records analyzed: 121
- Total persons involved: 276
- Total vehicles involved: 235
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). "MEDFORD, MA Crash Intelligence Report: January 2026." Published June 21, 2026. Reporting period: 2026-01-01 to 2026-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/medford/january-2026-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: 2026-01-01 – 2026-01-31
Generated: June 21, 2026 · All rights reserved