Yearly Traffic Safety Analysis

77 CRASHES IN
SHIRLEY, MA
2025

All metrics benchmarked against2024

In Shirley, total traffic crashes decreased by 3.8% from 80 incidents in 2024 to 77 in 2025. While overall crashes and injuries (down from 31 to 16) declined, the most notable year-over-year shift was an increase in crash severity, with the city recording one fatal crash in 2025 after having none in the prior year.

77

-3.8%was 80

Total Crash Events

1

Persons Killed

16

-48.4%was 31

Persons Injured

1

-50.0%was 2

Hit-and-Run Crashes

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Shirley showed a slight downward trend, with 77 incidents in 2025 compared to 80 in 2024, representing a 3.8% decrease. This drop in total crashes was accompanied by a significant 48.4% reduction in total injuries, from 31 to 16. However, the trend in severity worsened, as a crash in 2025 resulted in one fatality, whereas no fatalities were recorded in 2024.

1

Hit-and-Run Crashes — 2025

-50.0% vs prior (2)

The number of hit-and-run crashes decreased from two in 2024 to one in 2025. Correspondingly, the hit-and-run rate, as a percentage of all crashes, fell from 2.5% in the prior year to 1.3% in the current year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 0%

2

Pedestrians Injured

Prior: 1100.0%

2

Cyclists Injured

Prior: 0%

12

Motorists Injured

Prior: 30-60.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted between the two years. The peak day for crashes moved from a tie between Sunday and Monday (16 crashes each) in 2024 to Wednesday (14 crashes) in 2025. Similarly, the peak hour for crashes changed; in 2024, the highest frequency was tied at 7 a.m., 2 p.m., and 6 p.m. (7 crashes each), while in 2025, the peak was concentrated at 7 a.m. and 2 p.m., with an increased count of 8 crashes each.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes declined, the severity profile changed significantly year-over-year. In 2025, Shirley recorded one fatal crash, resulting in a fatal crash rate of 1.3%, up from zero in 2024. The overall proportion of crashes involving any level of injury (Serious, Minor, or Possible) decreased from 36.3% of all crashes in 2024 to 19.5% in 2025. Consequently, property-damage-only crashes increased as a share of the total, from 63.7% to 76.6%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.3%
Serious Injury4serious injury crashes5.2%
-20.0%prior 5
Minor Injury9minor injury crashes11.7%
-50.0%prior 18
Possible Injury2possible injury crashes2.6%
-66.7%prior 6
No Injury59no injury crashes76.6%
15.7%prior 51

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

The primary contributing factors for crashes shifted between periods. "Failed to yield right of way" became the leading factor in 2025 with 12 crashes, an increase from 10 crashes in 2024. This factor replaced "No improper driving" as the top-ranked cause, as its count fell from 21 to 11 incidents. The number of crashes attributed to "Inattention" also saw a notable increase in count, rising by 75% from 4 incidents in 2024 to 7 in 2025.

Officer-Reported Primary Contributing Cause

Failed to yield right of way12 (15.6%)20.0%prior 10
No improper driving11 (14.3%)-47.6%prior 21
Inattention7 (9.1%)
Failure to keep in proper lane or running off road7 (9.1%)0.0%prior 7
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner6 (7.8%)-33.3%prior 9
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway3 (3.9%)
Illness3 (3.9%)
Distracted3 (3.9%)
Other improper action2 (2.6%)
Physical impairment2 (2.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions were largely consistent across both years, with the majority of incidents occurring in clear weather and on dry roads. In 2025, 79.2% of crashes occurred on dry surfaces, similar to 81.3% in 2024. There was a minor shift in lighting conditions, as the proportion of crashes happening during daylight hours increased from 66.3% in 2024 to 74.0% in 2025.

Weather

Clear61 (79.2%)
3.4%prior 59
Cloudy4 (5.2%)
-33.3%prior 6
Snow2 (2.6%)
Clear/Clear2 (2.6%)
Rain/Cloudy1 (1.3%)
Rain/Severe crosswinds1 (1.3%)
Rain/Snow1 (1.3%)
Snow/Rain1 (1.3%)
Snow/Sleet, hail (freezing rain or drizzle)1 (1.3%)
Cloudy/Snow1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight57 (74.0%)
7.5%prior 53
Dark - roadway not lighted8 (10.4%)
-38.5%prior 13
Dark - lighted roadway6 (7.8%)
-25.0%prior 8
Dusk4 (5.2%)
Dark - unknown roadway lighting1 (1.3%)
Dawn1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry61 (79.2%)
-6.2%prior 65
Wet6 (7.8%)
20.0%prior 5
Snow5 (6.5%)
0.0%prior 5
Ice4 (5.2%)
Slush1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

Toyota remained the most frequently involved vehicle make in crashes, with its count increasing from 18 in 2024 to 22 in 2025, while Ford's involvement decreased from 17 vehicles to 10. The age demographics of persons involved in crashes also changed, most notably in the 65+ age group, which saw its involvement increase from 16 individuals in 2024 to 24 in 2025. The 16-20 age group also saw an increase, from 14 to 19 persons involved.

Top Vehicle Makes (115 vehicles)

1
TOYOTA22 (19.1%)
22.2%prior 18
2
CHEVROLET11 (9.6%)
0.0%prior 11
3
HONDA11 (9.6%)
-21.4%prior 14
4
FORD10 (8.7%)
-41.2%prior 17
5
SUBARU6 (5.2%)
6
NISSAN6 (5.2%)
-14.3%prior 7
7
KIA5 (4.3%)
8
HYUNDAI5 (4.3%)
9
BMW5 (4.3%)
10
JEEP5 (4.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (136 persons with recorded sex)

Male87 (64.0%)
11.5%prior 78
Female49 (36.0%)
-16.9%prior 59

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across different speed zones remained relatively stable year-over-year, with the 25 mph zone accounting for the most incidents in both periods (29 crashes in 2025 vs. 28 in 2024). Crashes in 30 mph and 40 mph zones saw slight decreases. The single fatal crash recorded in 2025 occurred within a 25 mph speed zone.

Fatal crashes by zone: 25 mph: 1 of 29 (3.448%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: SHIRLEY, MA
  • Total crash records analyzed: 77
  • Total persons involved: 138
  • Total vehicles involved: 115

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). "SHIRLEY, MA Crash Intelligence Report: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/shirley/2025-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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