ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PITTSFIELD, MA · 2023
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/2023-annual-report
Yearly Traffic Safety Analysis
928 CRASHES IN
PITTSFIELD, MA
2023
Total crashes in Pittsfield decreased from 958 in 2022 to 928 in 2023, a 3.1% reduction. Despite the overall decrease in collisions, the most significant year-over-year change was a sharp increase in traffic fatalities, which rose from one in 2022 to five in 2023.
928
▼ -3.1%was 958
Total Crash Events
5
▲ 400.0%was 1
Persons Killed
267
▼ -7.0%was 287
Persons Injured
4
▼ -33.3%was 6
Hit-and-Run Crashes
Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 57 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic collisions in Pittsfield saw a minor decrease of 3.1% from 958 in 2022 to 928 in 2023. This downward trend in total crashes was accompanied by a significant rise in severity. The number of fatalities increased from one to five, while total injuries fell by 7.0% from 287 to 267.
4
Hit-and-Run Crashes — 2023
▼ -33.3% vs prior (6)
The number of hit-and-run crashes decreased from 6 incidents in 2022 to 4 in 2023, a 33.3% reduction in the absolute count of such events. The hit-and-run rate, as a percentage of total crashes, also trended downward from 0.6% in the prior year to 0.4% in the current year.
Vulnerable Road User Casualties
3
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
15
Pedestrians Injured
10
Cyclists Injured
242
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The daily and weekly patterns of crashes showed some shifts between the two periods. While the afternoon rush hour at 4 p.m. remained the peak time for crashes in both years, the peak day for collisions moved from Monday in 2022 (158 crashes) to Friday in 2023 (162 crashes). Crashes on Mondays saw a notable year-over-year decrease to 124 incidents.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes declined, the severity of outcomes worsened in 2023. The number of fatal crashes increased from one to five, raising the fatal crash rate from 0.1% to 0.54% of all collisions. Conversely, crashes resulting in serious injuries decreased from 20 to 15, and those with possible injuries fell from 67 to 49.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors for crashes shifted between 2022 and 2023. "Inattention" saw its count increase by 32.1% from 109 to 144 incidents, moving it from the third to the second most cited factor. In contrast, crashes attributed to "Failed to yield right of way" decreased in count by 14.3% from 154 to 132, dropping from second to third place in the rankings.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The conditions under which crashes occurred remained largely consistent, with most incidents in both years happening in clear weather on dry roads. However, there was a notable decrease in crashes on adverse road surfaces. Collisions on snow-covered roads dropped from 55 in 2022 to 29 in 2023, and crashes on icy roads decreased from 15 to 9 over the same period.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes—Toyota, Ford, and Honda—remained the same in both years, though their total counts declined. Analysis of persons involved shows a demographic shift, with a decrease in the 16-20 and 21-25 age groups (from 222 to 175 and 227 to 188, respectively). Conversely, the number of people in the 26-34 age group involved in crashes increased from 317 to 367.
Top Vehicle Makes (1,698 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
153 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,864 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones changed slightly, with a notable increase in incidents occurring in 25 mph zones, from 146 in 2022 to 174 in 2023. While 2022's single fatal crash occurred in a 35 mph zone, the five fatal crashes in 2023 were spread across a wider range of zones, including 25, 30, 35, 40, and 45 mph areas.
Fatal crashes by zone: 25 mph: 1 of 174 (0.575%) · 30 mph: 1 of 370 (0.27%) · 35 mph: 1 of 243 (0.412%) · 40 mph: 1 of 67 (1.493%) · 45 mph: 1 of 12 (8.333%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: PITTSFIELD, MA
- Total crash records analyzed: 928
- Total persons involved: 2,023
- Total vehicles involved: 1,698
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: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/pittsfield/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved