ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PITTSFIELD, MA · NOVEMBER 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/pittsfield/november-2022-report
Monthly Traffic Safety Analysis
85 CRASHES IN
PITTSFIELD, MA
NOVEMBER 2022
PITTSFIELD, MA experienced a stable number of total crashes year-over-year, with 85 crashes recorded in November 2022, matching the 85 crashes in November 2021. Despite stable crash numbers, total injuries decreased by 20% from 30 in the prior period to 24 in the current period, with no serious injuries reported in the current month compared to two in the prior year.
85
Total Crash Events
0
Persons Killed
24
▼ -20.0%was 30
Persons Injured
1
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. 4 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Total crashes in PITTSFIELD, MA remained stable year-over-year, with 85 crashes reported in both November 2022 and November 2021. However, total injuries decreased by 20%, falling from 30 in November 2021 to 24 in November 2022, indicating a positive trend in crash severity outcomes.
1
Hit-and-Run Crashes — November 2022
1.2% hit-and-run rate this period vs 0.0% prior. Prior period: 0.
Vulnerable Road User Casualties
0
Cyclists Killed
0
Motorists Killed
3
Cyclists Injured
21
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · 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; the peak crash day moved from Tuesday with 17 crashes in November 2021 to Friday with 16 crashes in November 2022. The peak crash hour also shifted from 3 PM with 11 crashes in the prior period to 4 PM with 8 crashes in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatal crashes in either period. Total injuries decreased from 30 in November 2021 to 24 in November 2022, a 20% reduction, with serious injuries (code A) dropping from 2 to 0. Crashes resulting in no injury increased from 58 (68.2% share) to 62 (72.9% share), while possible injury crashes (code C) decreased from 8 (9.4% share) to 6 (7.1% share).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Most severe injury per crash record
Top Contributing Factors
Among contributing factors, 'Inattention' crashes increased by 6, from 6 in November 2021 to 12 in November 2022, representing a 100% rise. Crashes attributed to 'No improper driving' increased by 3, from 18 to 21, while 'Disregarded traffic signs, signals, road markings' decreased by 3, from 8 to 5, a 37.5% reduction. 'Driving too fast for conditions' crashes saw a 75% decrease, falling from 4 to 1.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in 'Daylight' conditions decreased by 12, from 53 in November 2021 to 41 in November 2022, while those in 'Dark - lighted roadway' increased by 8, from 21 to 29. Regarding road surface, crashes on 'Wet' surfaces increased by 7, from 6 to 13, contrasting with a decrease of 7 crashes on 'Snow' surfaces, from 9 to 2. Crashes in 'Clear' weather increased by 8, from 60 to 68, while those in 'Cloudy' conditions decreased by 7, from 11 to 4.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved remained stable, with 152 in November 2022 compared to 153 in November 2021. Toyota remained the top make, with its count increasing by 1 from 24 to 25, while Nissan's count decreased by 9, dropping from 21 to 12. The 26-34 age group saw an increase of 8 persons involved in crashes, from 21 to 29, while the 65+ age group experienced a decrease of 7 persons, from 29 to 22.
Top Vehicle Makes (152 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Vehicle unit records
11 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (164 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph speed zones increased by 7, from 8 in November 2021 to 15 in November 2022, an 87.5% rise. Conversely, crashes in 35 mph zones decreased by 9, from 26 to 17, a 34.6% reduction. The 40 mph speed zone experienced an increase of 4 crashes, from 5 to 9, while 45 mph zones appeared in the current period with 2 crashes, not being present in the prior period's data.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · 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: 2022-11-01 through 2022-11-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-11-01 through 2022-11-30 (30 days)
- Geographic scope: PITTSFIELD, MA
- Total crash records analyzed: 85
- Total persons involved: 176
- Total vehicles involved: 152
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). "PITTSFIELD, MA Crash Intelligence Report: November 2022." Published June 21, 2026. Reporting period: 2022-11-01 to 2022-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/pittsfield/november-2022-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: 2022-11-01 – 2022-11-30
Generated: June 21, 2026 · All rights reserved