Yearly Traffic Safety Analysis

77 CRASHES IN
LANESBOROUGH, MA
2023

All metrics benchmarked against2022

In 2023, Lanesborough recorded 77 total vehicle crashes, a 25.2% decrease from the 103 crashes reported in 2022. This overall reduction was accompanied by a significant 52% drop in total injuries, which fell from 50 in the prior year to 24 in the current year. Notably, crashes attributed to driving under the influence saw a substantial decrease, from 8 incidents in 2022 to just 1 in 2023.

77

-25.2%was 103

Total Crash Events

0

Persons Killed

24

-52.0%was 50

Persons Injured

4

100.0%was 2

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. 2 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

Crash data for Lanesborough shows a significant downward trend year-over-year. Total crashes fell by 25.2%, from 103 in 2022 to 77 in 2023. This decline was also reflected in the number of people injured, which decreased by 52% from 50 individuals in the prior year to 24 in the current period.

4

Hit-and-Run Crashes — 2023

100.0% vs prior (2)

The number of hit-and-run incidents increased from 2 in 2022 to 4 in 2023. This doubling in count, combined with the overall decrease in total crashes, resulted in the hit-and-run rate more than doubling. The rate rose from 1.9% of all crashes in 2022 to 5.2% in 2023, indicating an upward trend for this type of incident.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Cyclists Injured

Prior: 0%

23

Motorists Injured

Prior: 50-54.0%

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 temporal patterns of crashes showed some shifts between the two periods. In 2023, the peak days for crashes were Thursday and Friday, each with 13 incidents, a change from 2022 when Thursday was the clear peak with 24 crashes. The peak hour for collisions shifted slightly later, from 3 p.m. in 2022 (13 crashes) to a tie between 4 p.m. and 5 p.m. in 2023 (9 crashes each).

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

There were no fatal crashes recorded in either 2022 or 2023. The overall severity of crashes decreased, with the count of serious injury crashes falling from 4 in 2022 to 1 in 2023. Similarly, minor injury crashes were halved, dropping from 22 to 11. Consequently, the proportion of crashes resulting in no injuries increased from 70.9% of all incidents in 2022 to 77.9% in 2023.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.3%
-75.0%prior 4
Minor Injury11minor injury crashes14.3%
-50.0%prior 22
Possible Injury3possible injury crashes3.9%
0.0%prior 3
No Injury60no injury crashes77.9%
-17.8%prior 73

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

While 'No improper driving' remained the most common finding in both years, its count decreased from 40 crashes in 2022 to 30 in 2023. Crashes attributed to an 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' also saw a notable drop, from 19 incidents to 12. In contrast, crashes involving 'Inattention' held steady with 13 incidents in both years, making it the second most-cited factor in 2023, up from third in 2022.

Officer-Reported Primary Contributing Cause

No improper driving30 (39%)-25.0%prior 40
Inattention13 (16.9%)0.0%prior 13
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner12 (15.6%)-36.8%prior 19
Distracted3 (3.9%)
Other improper action3 (3.9%)
Glare2 (2.6%)
Failed to yield right of way2 (2.6%)
Physical impairment2 (2.6%)
Followed too closely2 (2.6%)-71.4%prior 7
Exceeded authorized speed limit1 (1.3%)

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

Driving conditions for crashes remained broadly similar year-over-year, with the majority of incidents in both periods occurring in clear weather and during daylight hours. In 2023, 77.9% of crashes happened on dry road surfaces, an increase from a 67.0% share in 2022. The proportion of crashes occurring in daylight also increased from 65.0% in 2022 to 72.7% in 2023.

Weather

Clear41 (53.2%)
-24.1%prior 54
Cloudy12 (15.6%)
20.0%prior 10
Clear/Unknown10 (13.0%)
-52.4%prior 21
Rain3 (3.9%)
Clear/Rain1 (1.3%)
Clear/Snow1 (1.3%)
Clear/Blowing sand, snow1 (1.3%)
Cloudy/Rain1 (1.3%)
Fog, smog, smoke1 (1.3%)
Fog, smog, smoke/Clear1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight56 (72.7%)
-16.4%prior 67
Dark - lighted roadway11 (14.3%)
-8.3%prior 12
Dark - roadway not lighted7 (9.1%)
-12.5%prior 8
Dusk2 (2.6%)
-75.0%prior 8
Dawn1 (1.3%)
-87.5%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry60 (77.9%)
-13.0%prior 69
Wet10 (13.0%)
-37.5%prior 16
Ice3 (3.9%)
Snow2 (2.6%)
-77.8%prior 9
Sand, mud, dirt, oil, gravel1 (1.3%)
Slush1 (1.3%)
-80.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The demographics of individuals involved in crashes shifted towards younger age groups in 2023. The 26-34 age group became the largest cohort with 34 individuals, up from 29 the previous year, while the 45-54 age group saw its involvement decrease from 36 to 17 people. Among vehicle makes, Toyota remained the most frequently involved brand in both years, though its count fell from 26 to 20. Ford's involvement was nearly halved, dropping from 21 vehicles in 2022 to 11 in 2023.

Top Vehicle Makes (121 vehicles)

1
TOYOTA20 (16.5%)
-23.1%prior 26
2
SUBARU14 (11.6%)
16.7%prior 12
3
CHEVROLET14 (11.6%)
27.3%prior 11
4
HONDA12 (9.9%)
-25.0%prior 16
5
FORD11 (9.1%)
-47.6%prior 21
6
NISSAN7 (5.8%)
40.0%prior 5
7
JEEP6 (5%)
-45.5%prior 11
8
HYUNDAI5 (4.1%)
-28.6%prior 7
9
DODGE4 (3.3%)
-42.9%prior 7
10
VOLKSWAGEN4 (3.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (154 persons with recorded sex)

Female79 (51.3%)
1.3%prior 78
Male75 (48.7%)
-35.9%prior 117

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

Crashes continued to be most prevalent in the 35 mph speed zone, with 28 incidents in 2023 compared to 32 in 2022. A significant reduction was observed in the 45 mph zone, where crash counts fell by more than half, from 30 in 2022 to 14 in 2023. Similarly, crashes in the 55 mph zone decreased from 10 to 6. There were no fatal crashes reported in any speed zone during either period.

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: LANESBOROUGH, MA
  • Total crash records analyzed: 77
  • Total persons involved: 163
  • Total vehicles involved: 121

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). "LANESBOROUGH, 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/lanesborough/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

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Lanesborough, MA Crash Report — 2023 | ThatCarHitMe.com