ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · READING, 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/reading/january-2026-report
Monthly Traffic Safety Analysis
42 CRASHES IN
READING, MA
JANUARY 2026
Total crashes in January 2026 increased to 42, up 31.25% from 32 crashes in January 2025. Total injuries also rose by 50%, from 6 to 9. A notable shift was observed in crashes occurring in 'Dark - lighted roadway' conditions, which increased from 4 to 17.
42
▲ 31.3%was 32
Total Crash Events
0
Persons Killed
9
▲ 50.0%was 6
Persons Injured
2
▼ -33.3%was 3
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 · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash data for January 2026 indicates an upward trend compared to the prior year. Total crashes rose by 31.25%, increasing from 32 to 42. Similarly, the number of injured persons increased by 50%, from 6 to 9.
2
Hit-and-Run Crashes — January 2026
▼ -33.3% vs prior (3)
The number of hit-and-run crashes decreased from 3 in January 2025 to 2 in January 2026. This represents a decrease in the hit-and-run crash rate from 9.4% to 4.8% of all crashes.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
1
Pedestrians Injured
8
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 temporal patterns of crashes shifted year-over-year. Monday became the peak day for crashes in January 2026 with 9 incidents, up from 2 in January 2025, displacing Tuesday which had 8 crashes in the prior period. The peak hour for crashes also moved, with 5 PM recording 7 crashes in January 2026 compared to 2 crashes at that hour in January 2025, while 12 PM was the peak hour in the prior period with 4 crashes.
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
No fatalities were recorded in either January 2026 or January 2025. Serious injury crashes (severity A) increased by 100%, from 1 in the prior period to 2 in the current period, representing 4.8% of current crashes. Minor injury crashes (severity B) also rose from 2 to 5, while possible injury crashes (severity C) decreased from 2 to 1.
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
Contributing factors saw significant shifts, with 'Followed too closely' increasing from 4 crashes to 11 crashes, a 175% rise in count, becoming the top factor in the current period. 'Driving too fast for conditions' also increased from 2 crashes to 5 crashes, a 150% increase in count. Conversely, 'Inattention' decreased from 5 crashes to 3 crashes, a 40% decrease in count, and 'No improper driving' decreased from 6 crashes to 3 crashes, a 50% decrease in count.
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
Crashes occurring in 'Dark - lighted roadway' conditions increased substantially from 4 in the prior period to 17 in the current period, an increase of 13 crashes. Crashes on 'Dry' road surfaces increased from 21 to 25, while crashes on 'Wet' surfaces increased from 5 to 9. The number of crashes occurring at 'Dusk' decreased from 4 to 1, and 'Dawn' crashes, which accounted for 3 incidents in the prior period, were not present in the current period.
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 73 to 88 year-over-year. Honda became the top vehicle make involved in crashes, increasing from 5 to 15, while Toyota's involvement decreased from 15 to 11. The 55-64 age group saw a notable increase in persons involved, rising from 9 to 22, and the 16-20 age group also increased from 7 to 13 persons.
Top Vehicle Makes (88 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Vehicle unit records
17 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (114 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 35 MPH speed zones increased from 1 to 5, and in 65 MPH zones, crashes rose from 3 to 7. Crashes in 30 MPH zones increased from 13 to 15, and in 55 MPH zones, they increased from 5 to 7. Conversely, crashes in 40 MPH zones decreased from 7 to 3. No fatalities were recorded in any speed zone for either period.
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: READING, MA
- Total crash records analyzed: 42
- Total persons involved: 123
- Total vehicles involved: 88
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). "READING, 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/reading/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