ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PITTSFIELD, MA · MAY 2024
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/may-2024-report
Monthly Traffic Safety Analysis
82 CRASHES IN
PITTSFIELD, MA
MAY 2024
In May 2024, Pittsfield experienced 82 total crashes, a decrease of 8 crashes compared to 90 crashes in May 2023, representing an 8.9% reduction. Despite the decrease in total crashes, hit-and-run incidents saw a substantial increase, rising from 1 crash in May 2023 to 5 crashes in May 2024, a 400% increase.
82
▼ -8.9%was 90
Total Crash Events
2
▲ 100.0%was 1
Persons Killed
29
▲ 81.3%was 16
Persons Injured
5
▲ 400.0%was 1
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 3 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, the total number of crashes in Pittsfield decreased by 8.9% year-over-year, from 90 crashes in May 2023 to 82 crashes in May 2024. However, total fatalities doubled from 1 in May 2023 to 2 in May 2024, and total injuries increased by 81.3%, from 16 to 29.
5
Hit-and-Run Crashes — May 2024
▲ 400.0% vs prior (1)
Hit-and-run crashes increased substantially from 1 incident in May 2023 to 5 incidents in May 2024, representing a 400% increase. The hit-and-run rate also rose significantly, from 1.1% of all crashes in May 2023 to 6.1% in May 2024, indicating an upward trend.
Vulnerable Road User Casualties
0
Cyclists Killed
1
Motorists Killed
1
Other Killed
3
Cyclists Injured
26
Motorists Injured
0
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal distribution of crashes shifted year-over-year; Monday became the peak day for crashes in May 2024 with 18 incidents, up from 15 in May 2023, while Tuesday crashes significantly decreased from 22 to 9. The peak crash hour also shifted from 3 PM with 12 crashes in May 2023 to 12 PM with 10 crashes in May 2024.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The fatal crash rate increased from 1.11% in May 2023 to 2.44% in May 2024, with the number of fatal crashes doubling from 1 to 2. Injury crashes also saw a notable increase in proportion, with minor injury crashes rising from 10% to 13.4% and possible injury crashes increasing from 3.3% to 9.8% of total incidents.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Most severe injury per crash record
Top Contributing Factors
Contributing factors saw shifts in prevalence year-over-year. 'No improper driving' increased by 10 incidents, from 13 in May 2023 to 23 in May 2024, representing a 76.9% rise. Conversely, 'Followed too closely' decreased significantly by 8 incidents, from 10 to 2, an 80% reduction, and 'Failed to yield right of way' decreased by 3 incidents, from 16 to 13.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
While clear weather remained the dominant condition for crashes, incidents in clear weather decreased from 78 in May 2023 to 68 in May 2024. Crashes occurring during rain increased from 2 to 4, and incidents in 'Dark - lighted roadway' conditions increased from 9 to 12 year-over-year.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased by 6.7%, from 165 in May 2023 to 154 in May 2024. There was a notable shift in age distribution, with persons aged 16-20 involved in 10 fewer incidents (20 to 10) and those aged 55-64 involved in 10 fewer incidents (34 to 24), while persons aged 65+ increased by 9 incidents (23 to 32). Toyota became the top vehicle make involved in crashes, increasing from 19 to 25, surpassing Honda, which saw its count decrease from 24 to 12.
Top Vehicle Makes (154 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Vehicle unit records
17 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (161 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph zones decreased from 21 to 14, and in 35 mph zones from 27 to 18. Conversely, crashes in 30 mph zones slightly increased from 30 to 32, and a fatal crash occurred in a 30 mph zone in May 2024, where none occurred in May 2023. Crashes in 40 mph zones increased from 5 to 7, with one fatal crash occurring in this zone in both periods.
Fatal crashes by zone: 30 mph: 1 of 32 (3.125%) · 40 mph: 1 of 7 (14.286%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · 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: 2024-05-01 through 2024-05-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-05-01 through 2024-05-31 (31 days)
- Geographic scope: PITTSFIELD, MA
- Total crash records analyzed: 82
- Total persons involved: 179
- Total vehicles involved: 154
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: May 2024." Published June 21, 2026. Reporting period: 2024-05-01 to 2024-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/pittsfield/may-2024-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: 2024-05-01 – 2024-05-31
Generated: June 21, 2026 · All rights reserved