Monthly Traffic Safety Analysis

60 CRASHES IN
PITTSFIELD, MA
APRIL 2023

All metrics benchmarked againstApril 2022

Total crashes in PITTSFIELD, MA decreased by 16.7%, from 72 in April 2022 to 60 in April 2023. This period also saw a significant reduction in total injuries, which fell by 44% year-over-year. The most notable shift was the decrease in the number of persons involved in crashes from 155 to 121.

60

-16.7%was 72

Total Crash Events

0

Persons Killed

14

-44.0%was 25

Persons Injured

0

-100.0%was 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. 5 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash activity in PITTSFIELD, MA decreased year-over-year. Total crashes fell from 72 in April 2022 to 60 in April 2023, representing a 16.7% reduction. Similarly, total injuries decreased by 44%, from 25 to 14.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Pedestrians Injured

Prior: 10.0%

1

Cyclists Injured

Prior: 0%

12

Motorists Injured

Prior: 24-50.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

In April 2023, the peak crash days were Monday and Tuesday, both with 11 crashes, and the peak hour was 2 p.m. with 8 crashes. This represents a shift from April 2022, when Friday and Tuesday were the peak days with 13 crashes each, and 5 p.m. was the peak hour with 6 crashes. Overall, crash counts were lower across most days of the week in April 2023 compared to the prior year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatalities reported in either April 2023 or April 2022. Serious injuries decreased by 50%, from 4 crashes (5.6% of total) in April 2022 to 2 crashes (3.3% of total) in April 2023. Possible injuries also decreased by 50%, from 6 crashes (8.3% of total) to 3 crashes (5% of total) year-over-year.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes3.3%
-50.0%prior 4
Minor Injury9minor injury crashes15%
0.0%prior 9
Possible Injury3possible injury crashes5%
-50.0%prior 6
No Injury41no injury crashes68.3%
-22.6%prior 53

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Most severe injury per crash record

Top Contributing Factors

The leading factor, "No improper driving," decreased from 17 crashes to 15 crashes. "Failed to yield right of way" decreased from 12 crashes to 9 crashes. "Operating vehicle in an erratic, reckless, careless, negligent or aggressive manner" saw the largest decrease, dropping from 6 crashes to 1 crash. Conversely, "Inattention" increased from 5 crashes to 7 crashes.

Officer-Reported Primary Contributing Cause

No improper driving15 (25%)-11.8%prior 17
Failed to yield right of way9 (15%)-25.0%prior 12
Inattention7 (11.7%)40.0%prior 5
Followed too closely5 (8.3%)-16.7%prior 6
Distracted3 (5%)
Other improper action3 (5%)
Failure to keep in proper lane or running off road2 (3.3%)
Fatigued/asleep1 (1.7%)
Made an improper turn1 (1.7%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (1.7%)-83.3%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in daylight decreased from 56 in April 2022 to 45 in April 2023. Crashes on dry road surfaces also decreased from 58 to 51. Crashes during wet road conditions decreased from 13 to 9.

Weather

Clear49 (81.7%)
2.1%prior 48
Cloudy5 (8.3%)
-37.5%prior 8
Cloudy/Rain3 (5.0%)
-50.0%prior 6
Rain2 (3.3%)
-66.7%prior 6
Rain/Cloudy1 (1.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Weather condition at time of crash

Lighting

Daylight45 (75.0%)
-19.6%prior 56
Dark - lighted roadway11 (18.3%)
-8.3%prior 12
Dark - roadway not lighted4 (6.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Lighting condition field

Road Surface

Dry51 (85.0%)
-12.1%prior 58
Wet9 (15.0%)
-30.8%prior 13

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Road surface condition field

Vehicles & Demographics

Toyota remained the top vehicle make involved, decreasing slightly from 19 to 18. Nissan saw an increase in involvement from 10 vehicles in April 2022 to 17 in April 2023, moving to the second position. The 16-20 age group experienced a significant reduction in persons involved, from 25 to 7, while the 21-25 age group decreased from 22 to 15.

Top Vehicle Makes (104 vehicles)

1
TOYOTA18 (17.3%)
-5.3%prior 19
2
NISSAN17 (16.3%)
70.0%prior 10
3
FORD11 (10.6%)
10.0%prior 10
4
SUBARU8 (7.7%)
-27.3%prior 11
5
HONDA8 (7.7%)
-33.3%prior 12
6
CHEVROLET7 (6.7%)
-46.2%prior 13
7
VOLKSWAGEN4 (3.8%)
8
JEEP4 (3.8%)
-20.0%prior 5
9
GMC3 (2.9%)
-40.0%prior 5
10
DODGE3 (2.9%)
-50.0%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Vehicle unit records

14 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (105 persons with recorded sex)

Male57 (54.3%)
-24.0%prior 75
Female47 (44.8%)
-28.8%prior 66
X / Unspecified1 (1.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 25 mph zones increased from 10 in April 2022 to 16 in April 2023. Crashes in 30 mph zones increased slightly from 29 to 31. Conversely, crashes in 35 mph zones decreased from 19 to 11. No fatalities were reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · 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: 2023-04-01 through 2023-04-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-04-01 through 2023-04-30 (30 days)
  • Geographic scope: PITTSFIELD, MA
  • Total crash records analyzed: 60
  • Total persons involved: 121
  • Total vehicles involved: 104

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: April 2023." Published June 21, 2026. Reporting period: 2023-04-01 to 2023-04-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/pittsfield/april-2023-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

Pittsfield, MA Crash Report — April 2023 | ThatCarHitMe.com