Monthly Traffic Safety Analysis

53 CRASHES IN
SWANSEA, MA
SEPTEMBER 2023

All metrics benchmarked againstSeptember 2022

In September 2023, Swansea experienced 53 total crashes, a 7.02% decrease from the 57 crashes recorded in September 2022. Total fatalities remained constant at 1 in both periods, while total injuries slightly decreased from 22 to 21. The most notable shift was a significant increase in hit-and-run crashes, rising from 3 in the prior period to 7 in the current period.

53

-7.0%was 57

Total Crash Events

1

Persons Killed

21

-4.5%was 22

Persons Injured

7

133.3%was 3

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 · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in Swansea saw a slight downward trend year-over-year, with total crashes decreasing from 57 in September 2022 to 53 in September 2023, representing a 7.02% reduction. Fatalities remained stable at 1 in both periods, while total injuries decreased by 1, from 22 to 21. This indicates a minor improvement in overall crash frequency, but with consistent fatal outcomes.

7

Hit-and-Run Crashes — September 2023

133.3% vs prior (3)

Hit-and-run crashes saw a substantial increase, rising from 3 incidents in September 2022 to 7 incidents in September 2023, an increase of 4 crashes. This led to the hit-and-run rate more than doubling, from 5.3% in the prior period to 13.2% in the current period, an increase of 7.9 percentage points.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 0%

21

Motorists Injured

Prior: 210.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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 Thursday in September 2022, with 12 crashes, to Friday in September 2023, also with 12 crashes. The peak hour also shifted, from 3p with 8 crashes in the prior period to 2p with 7 crashes in the current period. Crashes on Tuesday decreased by 5, from 9 to 4, while crashes on Wednesday increased by 4, from 5 to 9.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The number of fatal crashes remained constant at 1 in both September 2022 and September 2023, though the fatal crash rate slightly increased from 1.75% to 1.89%. Serious Injury (A) crashes were reported in September 2023 with 2 incidents, while no such crashes were explicitly listed in the prior period's severity breakdown. Minor Injury (B) crashes decreased from 12 in September 2022 to 8 in September 2023, and Possible Injury (C) crashes decreased from 3 to 2.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.9%
0.0%prior 1
Serious Injury2serious injury crashes3.8%
Minor Injury8minor injury crashes15.1%
-33.3%prior 12
Possible Injury2possible injury crashes3.8%
-33.3%prior 3
No Injury38no injury crashes71.7%
-7.3%prior 41

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, 'Failed to yield right of way' increased by 2 crashes, from 11 in September 2022 to 13 in September 2023, representing an 18.18% increase in count. 'No improper driving' and 'Followed too closely' each decreased by 1 crash, from 10 to 9, a 10% decrease in count for both. 'Inattention' increased by 1 crash, from 6 to 7, a 16.67% increase in count, and 'Disregarded traffic signs, signals, road markings' increased by 1 crash, from 2 to 3, a 50% increase in count. Conversely, 'Failure to keep in proper lane or running off road' decreased by 2 crashes, from 5 to 3, a 40% decrease in count.

Officer-Reported Primary Contributing Cause

Failed to yield right of way13 (24.5%)18.2%prior 11
No improper driving9 (17%)-10.0%prior 10
Followed too closely9 (17%)-10.0%prior 10
Inattention7 (13.2%)16.7%prior 6
Disregarded traffic signs, signals, road markings3 (5.7%)
Failure to keep in proper lane or running off road3 (5.7%)-40.0%prior 5
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (3.8%)
Exceeded authorized speed limit1 (1.9%)
Operating defective equipment1 (1.9%)
Driving too fast for conditions1 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased by 10, from 42 in September 2022 to 32 in September 2023, while crashes in 'Cloudy' conditions increased by 6, from 1 to 7. Crashes during 'Daylight' increased by 3, from 44 to 47, while those in 'Dark - lighted roadway' decreased by 5, from 8 to 3. Regarding road surface, crashes on 'Dry' roads decreased by 8, from 47 to 39, whereas crashes on 'Wet' roads increased by 4, from 9 to 13.

Weather

Clear32 (61.5%)
-23.8%prior 42
Cloudy7 (13.5%)
Cloudy/Rain6 (11.5%)
Rain5 (9.6%)
-16.7%prior 6
Clear/Other1 (1.9%)
-80.0%prior 5
Cloudy/Unknown1 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash

Lighting

Daylight47 (90.4%)
6.8%prior 44
Dark - lighted roadway3 (5.8%)
-62.5%prior 8
Dark - roadway not lighted2 (3.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field

Road Surface

Dry39 (75.0%)
-17.0%prior 47
Wet13 (25.0%)
44.4%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased by 5, from 105 in September 2022 to 100 in September 2023. Toyota remained the top make, with its involvement increasing by 1 vehicle from 20 to 21. Honda and Nissan involvement each increased by 3 vehicles, while Ford and Chevrolet involvement each decreased by 3 and 2 vehicles, respectively. The age distribution of persons involved showed a decrease of 6 persons in the 0-15 age group and 11 persons in the 45-54 age group, while the 35-44 age group saw an increase of 4 persons. Male involvement decreased by 18 persons, from 68 to 50, a more significant drop than the 3-person decrease for females.

Top Vehicle Makes (100 vehicles)

1
TOYOTA21 (21%)
5.0%prior 20
2
HONDA11 (11%)
37.5%prior 8
3
NISSAN10 (10%)
42.9%prior 7
4
HYUNDAI7 (7%)
-12.5%prior 8
5
FORD5 (5%)
-37.5%prior 8
6
SUBARU4 (4%)
7
JEEP4 (4%)
-20.0%prior 5
8
ACURA3 (3%)
9
BMW3 (3%)
10
CHEVROLET3 (3%)
-40.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records

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

Sex Distribution (103 persons with recorded sex)

Female53 (51.5%)
-5.4%prior 56
Male50 (48.5%)
-26.5%prior 68

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events

Speed Limit Zones

Fatal crashes occurred in different speed zones year-over-year, with one fatal crash in a 40 mph zone in September 2022, and one fatal crash in a 30 mph zone in September 2023. Crashes in 10 mph zones increased by 3, from 1 to 4, and in 15 mph zones by 4, from 1 to 5. Conversely, crashes in 35 mph zones decreased by 6, from 11 to 5, and in 65 mph zones by 2, from 6 to 4.

Fatal crashes by zone: 30 mph: 1 of 6 (16.667%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-09-01 through 2023-09-30 (30 days)
  • Geographic scope: SWANSEA, MA
  • Total crash records analyzed: 53
  • Total persons involved: 116
  • Total vehicles involved: 100

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). "SWANSEA, MA Crash Intelligence Report: September 2023." Published June 21, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/swansea/september-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

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Swansea, MA Crash Report — September 2023 | ThatCarHitMe.com