Monthly Traffic Safety Analysis

50 CRASHES IN
PITTSFIELD, MA
FEBRUARY 2024

All metrics benchmarked againstFebruary 2023

In February 2024, PITTSFIELD, MA experienced 50 crashes, a decrease from the 65 crashes reported in February 2023. This represents a 23.08% reduction in total crashes year-over-year. The most notable change was a 75% decrease in total injuries, falling from 16 to 4.

50

-23.1%was 65

Total Crash Events

0

Persons Killed

4

-75.0%was 16

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. 5 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for February 2024 indicates a downward trend compared to the same month in the prior year. Total crashes decreased by 15, from 65 to 50, representing a 23.08% reduction. Total injuries also saw a significant decrease, falling from 16 to 4.

1

Hit-and-Run Crashes — February 2024

2.0% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

4

Motorists Injured

Prior: 15-73.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Friday, with 14 crashes in February 2023, to Thursday, with 17 crashes in February 2024. The peak hour also changed, moving from 5 p.m. (8 crashes) in February 2023 to 7 a.m. (6 crashes) in February 2024.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Both periods reported 0 fatalities. Total injuries decreased significantly from 16 in February 2023 to 4 in February 2024, a 75% reduction. In February 2023, there was 1 serious injury, 10 minor injuries, and 2 possible injuries, while in February 2024, there were 2 minor injuries and 2 possible injuries, with no serious injuries reported.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes4%
-80.0%prior 10
Possible Injury2possible injury crashes4%
0.0%prior 2
No Injury41no injury crashes82%
-16.3%prior 49

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' increased from 14 in February 2023 to 16 in February 2024, with its share of total crashes rising from 21.5% to 32%. Conversely, 'Failed to yield right of way' crashes decreased from 13 to 7, and 'Distracted' crashes decreased from 5 to 2. 'Inattention' crashes remained constant at 7 in both periods.

Officer-Reported Primary Contributing Cause

No improper driving16 (32%)14.3%prior 14
Inattention7 (14%)0.0%prior 7
Failed to yield right of way7 (14%)-46.2%prior 13
Followed too closely6 (12%)
Disregarded traffic signs, signals, road markings2 (4%)
Distracted2 (4%)-60.0%prior 5
History heart/epilepsy/fainting1 (2%)
Made an improper turn1 (2%)
Fatigued/asleep1 (2%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The number of crashes occurring in 'Clear' weather decreased from 42 in February 2023 to 36 in February 2024, while 'Snow' condition crashes decreased from 7 to 1. Crashes on 'Wet' road surfaces decreased from 11 to 5. Crashes in 'Dark - lighted roadway' conditions decreased from 22 in February 2023 to 11 in February 2024, while 'Daylight' crashes remained constant at 35 in both periods.

Weather

Clear36 (72.0%)
-14.3%prior 42
Cloudy8 (16.0%)
0.0%prior 8
Clear/Snow1 (2.0%)
Clear/Unknown1 (2.0%)
Clear/Rain1 (2.0%)
Cloudy/Other1 (2.0%)
Rain1 (2.0%)
Snow1 (2.0%)
-85.7%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Weather condition at time of crash

Lighting

Daylight35 (70.0%)
0.0%prior 35
Dark - lighted roadway11 (22.0%)
-50.0%prior 22
Dark - roadway not lighted2 (4.0%)
Dawn2 (4.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Lighting condition field

Road Surface

Dry43 (86.0%)
-4.4%prior 45
Wet5 (10.0%)
-54.5%prior 11
Snow2 (4.0%)
-60.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 114 in February 2023 to 91 in February 2024. Toyota remained the most frequently involved vehicle make, though its count decreased from 19 to 15. Honda and Chevrolet each accounted for 11 vehicles in February 2024, whereas in February 2023, Hyundai (13 vehicles) and Nissan (12 vehicles) held the second and third positions, respectively.

Top Vehicle Makes (91 vehicles)

1
TOYOTA15 (16.5%)
-21.1%prior 19
2
HONDA11 (12.1%)
83.3%prior 6
3
CHEVROLET11 (12.1%)
37.5%prior 8
4
SUBARU9 (9.9%)
-10.0%prior 10
5
DODGE5 (5.5%)
6
HYUNDAI4 (4.4%)
-69.2%prior 13
7
NISSAN4 (4.4%)
-66.7%prior 12
8
RAM4 (4.4%)
9
JEEP4 (4.4%)
-60.0%prior 10
10
AUDI3 (3.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Vehicle unit records

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

Sex Distribution (97 persons with recorded sex)

Male53 (54.6%)
-13.1%prior 61
Female44 (45.4%)
-24.1%prior 58

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones remained relatively stable, decreasing slightly from 23 in February 2023 to 22 in February 2024. Crashes in 35 mph zones saw a notable decrease from 16 to 10. There were no fatal crashes reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · 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: 2024-02-01 through 2024-02-29
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-02-01 through 2024-02-29 (29 days)
  • Geographic scope: PITTSFIELD, MA
  • Total crash records analyzed: 50
  • Total persons involved: 101
  • Total vehicles involved: 91

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: February 2024." Published June 21, 2026. Reporting period: 2024-02-01 to 2024-02-29. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/pittsfield/february-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

Pittsfield, MA Crash Report — February 2024 | ThatCarHitMe.com