ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PITTSFIELD, MA · 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/2022-annual-report
Yearly Traffic Safety Analysis
958 CRASHES IN
PITTSFIELD, MA
2022
In Pittsfield, total traffic crashes increased by 7.2%, rising from 894 incidents in 2021 to 958 in 2022. Despite the higher crash volume, the number of fatalities fell from three to one during the same period. A notable increase was observed in serious injury crashes, which rose from 14 to 20 year-over-year.
958
▲ 7.2%was 894
Total Crash Events
1
▼ -66.7%was 3
Persons Killed
287
▲ 5.5%was 272
Persons Injured
6
▼ -25.0%was 8
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. 43 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend shows an increase in crash frequency, with total collisions rising by 7.2% from 894 in 2021 to 958 in 2022. The number of people injured also saw a slight increase of 5.5%, from 272 to 287. However, the number of fatalities reported saw a significant decrease, falling from three in the prior year to one in the current year.
6
Hit-and-Run Crashes — 2022
▼ -25.0% vs prior (8)
The data indicates a downward trend in hit-and-run incidents. The total number of hit-and-run crashes decreased from 8 in 2021 to 6 in 2022. Correspondingly, the rate of hit-and-run crashes relative to all collisions also declined, falling from 0.9% in the prior year to 0.6% in the current year.
Vulnerable Road User Casualties
0
Pedestrians Killed
1
Cyclists Killed
0
Motorists Killed
0
Other Killed
20
Pedestrians Injured
14
Cyclists Injured
249
Motorists Injured
4
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The daily and hourly patterns of crashes remained broadly consistent year-over-year, concentrated on weekdays and in the afternoon. The peak day for crashes shifted from Friday (156 crashes) in 2021 to Monday (158 crashes) in 2022. The peak hour also shifted slightly later, from the 3 p.m. hour (94 crashes) in 2021 to the 4 p.m. hour (87 crashes) in 2022.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes increased, the number of fatal crashes decreased from three in 2021 to one in 2022, lowering the fatal crash rate from 0.3% to 0.1% of all incidents. Conversely, the count of crashes resulting in serious injuries increased from 14 to 20, and minor injury crashes rose from 108 to 133. This represents a shift towards a higher proportion of crashes involving non-fatal injuries, with serious injury crashes growing from 1.6% to 2.1% of all incidents.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors remained consistent between periods, with "Failed to yield right of way" and "Inattention" being the top driver-related causes after "No improper driving." The count of crashes attributed to failing to yield increased from 145 to 154, while inattention-related crashes saw a slight increase from 107 to 109. Notably, the count of crashes involving a driver swerving or avoiding an object rose by 83%, from 12 incidents in 2021 to 22 in 2022.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in both years predominantly occurred in clear weather and on dry roads. The number of crashes during clear weather increased from 601 to 705, a rise consistent with the overall increase in collisions. The proportion of crashes happening on wet road surfaces decreased slightly from 14.7% in 2021 to 13.0% in 2022. Lighting conditions showed no significant proportional shift, with about 73% of crashes in 2022 and 71% in 2021 occurring in daylight.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes remained Toyota, Honda, and Ford in both years, with their relative order changing slightly as Ford's involvement grew from 165 to 187 vehicles. Toyota was the most common make in both 2021 (248 vehicles) and 2022 (261 vehicles). Analysis of persons involved shows a shift in demographics; involvement for the 26-34 age group decreased from 362 to 317 persons, while the 35-44 age group's involvement increased from 300 to 322 persons.
Top Vehicle Makes (1,746 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
146 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,933 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones shifted between the two periods. Collisions in 30 mph zones, the most frequent location in 2021 (423 crashes), decreased to 381 crashes in 2022. Conversely, crashes in 25 mph zones increased significantly from 100 to 146 incidents. In 2022, the single fatal crash occurred in a 35 mph zone, whereas in 2021, two fatalities occurred in 30 mph zones and one in a 35 mph zone.
Fatal crashes by zone: 35 mph: 1 of 240 (0.417%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: PITTSFIELD, MA
- Total crash records analyzed: 958
- Total persons involved: 2,080
- Total vehicles involved: 1,746
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: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/pittsfield/2022-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: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved