Monthly Traffic Safety Analysis

66 CRASHES IN
DARTMOUTH, MA
MAY 2022

All metrics benchmarked againstMay 2021

DARTMOUTH experienced a notable increase in crash activity in May 2022 compared to May 2021, with total crashes rising from 54 to 66, representing a 22.2% increase. Total injuries also saw an increase from 29 to 34. The most significant year-over-year shift was a 300% increase in DUI crashes, rising from 1 to 4.

66

22.2%was 54

Total Crash Events

0

Persons Killed

34

17.2%was 29

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for DARTMOUTH indicates an upward trend year-over-year, with total crashes increasing by 22.2% from 54 in May 2021 to 66 in May 2022. Concurrently, the total number of persons injured in crashes rose by 17.2%, from 29 to 34. There were no fatalities reported in either period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

34

Motorists Injured

Prior: 2821.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · 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 Wednesday with 10 crashes in May 2021 to Tuesday with 14 crashes in May 2022. The peak hour also changed, moving from 5 PM with 6 crashes in the prior period to 2 PM with 7 crashes in the current period. This indicates a shift in the busiest times for crash occurrences.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at zero in both May 2021 and May 2022. The proportion of minor injury crashes increased from 25.9% to 30.3% of all crashes. Conversely, possible injury crashes decreased from 14.8% to 9.1% of all crashes, despite an overall increase in total injuries from 29 to 34.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.5%
0.0%prior 1
Minor Injury20minor injury crashes30.3%
42.9%prior 14
Possible Injury6possible injury crashes9.1%
-25.0%prior 8
No Injury37no injury crashes56.1%
27.6%prior 29

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, 'Inattention' saw a substantial increase from 5 crashes in May 2021 to 12 crashes in May 2022, a 140% rise in count. 'Failure to keep in proper lane or running off road' decreased from 11 crashes to 8 crashes, a 27.3% reduction in count. 'Failed to yield right of way' also increased from 4 crashes to 7 crashes, a 75% increase in count.

Officer-Reported Primary Contributing Cause

Inattention12 (18.2%)140.0%prior 5
No improper driving11 (16.7%)10.0%prior 10
Failure to keep in proper lane or running off road8 (12.1%)-27.3%prior 11
Failed to yield right of way7 (10.6%)
Distracted3 (4.5%)
Fatigued/asleep3 (4.5%)
Disregarded traffic signs, signals, road markings3 (4.5%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (3%)
Visibility obstructed2 (3%)
Driving too fast for conditions2 (3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions increased from 41 to 50, while those during rain increased from 4 to 5. Daylight crashes rose from 37 to 50, whereas crashes in unlighted dark conditions decreased from 9 to 4. Crashes on dry roads increased from 46 to 57, and those on wet roads increased from 7 to 9.

Weather

Clear50 (76.9%)
22.0%prior 41
Cloudy7 (10.8%)
40.0%prior 5
Rain5 (7.7%)
Rain/Cloudy1 (1.5%)
Fog, smog, smoke1 (1.5%)
Clear/Cloudy1 (1.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Weather condition at time of crash

Lighting

Daylight50 (75.8%)
35.1%prior 37
Dark - lighted roadway8 (12.1%)
33.3%prior 6
Dark - roadway not lighted4 (6.1%)
-55.6%prior 9
Dusk2 (3.0%)
Dark - unknown roadway lighting1 (1.5%)
Dawn1 (1.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Lighting condition field

Road Surface

Dry57 (86.4%)
23.9%prior 46
Wet9 (13.6%)
28.6%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 85 in May 2021 to 109 in May 2022. Toyota and Honda remained the top two vehicle makes involved, with Toyota increasing from 14 to 15 and Honda from 11 to 15. The 16-20 age group saw a significant increase in persons involved, from 13 to 27, while the 26-34 age group increased from 9 to 24.

Top Vehicle Makes (109 vehicles)

1
TOYOTA15 (13.8%)
7.1%prior 14
2
HONDA15 (13.8%)
36.4%prior 11
3
FORD11 (10.1%)
57.1%prior 7
4
KIA8 (7.3%)
5
HYUNDAI6 (5.5%)
6
MERCEDES-BENZ5 (4.6%)
7
GMC5 (4.6%)
8
CHEVROLET5 (4.6%)
-37.5%prior 8
9
DODGE4 (3.7%)
10
NISSAN3 (2.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Vehicle unit records

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

Sex Distribution (136 persons with recorded sex)

Male69 (50.7%)
27.8%prior 54
Female67 (49.3%)
36.7%prior 49

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones increased from 14 to 16, and in 35 mph zones from 7 to 10. Crashes in 40 mph zones also saw an increase from 15 to 17. The number of crashes in 65 mph zones remained stable at 11 in both periods, and no fatal crashes were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · 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: 2022-05-01 through 2022-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-05-01 through 2022-05-31 (31 days)
  • Geographic scope: DARTMOUTH, MA
  • Total crash records analyzed: 66
  • Total persons involved: 145
  • Total vehicles involved: 109

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). "DARTMOUTH, MA Crash Intelligence Report: May 2022." Published June 21, 2026. Reporting period: 2022-05-01 to 2022-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/dartmouth/may-2022-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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Dartmouth, MA Crash Report — May 2022 | ThatCarHitMe.com