ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · DARTMOUTH, MA · OCTOBER 2022
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/dartmouth/october-2022-report
Monthly Traffic Safety Analysis
55 CRASHES IN
DARTMOUTH, MA
OCTOBER 2022
In October 2022, DARTMOUTH experienced 55 total crashes, a 5.17% decrease from the 58 crashes reported in October 2021. Despite this slight reduction in total crashes, total injuries increased significantly from 12 to 25, marking a 108.33% rise year-over-year. A notable positive shift was the absence of fatalities in October 2022, down from 1 fatality in October 2021.
55
▼ -5.2%was 58
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
25
▲ 108.3%was 12
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. 1 crash with unreported severity is not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall number of crashes in DARTMOUTH decreased by 5.17% from 58 to 55 year-over-year. However, total injuries more than doubled, rising from 12 to 25, while fatalities were eliminated, dropping from 1 to 0. This indicates a shift towards a higher incidence of injury-producing crashes, despite fewer overall incidents and no fatal outcomes.
1
Hit-and-Run Crashes — October 2022
1.8% hit-and-run rate this period vs 0.0% prior. Prior period: 0.
Vulnerable Road User Casualties
0
Motorists Killed
25
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal pattern of crashes shifted year-over-year. The peak day for crashes moved from Saturday with 10 crashes in October 2021 to Wednesday with 11 crashes in October 2022. Similarly, the peak hour for crashes changed from 4 PM with 9 crashes in October 2021 to 8 AM with 6 crashes in October 2022.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity distribution of crashes saw significant changes, with fatalities decreasing from 1 to 0 and the fatal crash rate dropping from 1.72% to 0%. Total injuries increased from 12 to 25. Minor injury crashes (severity B) rose from 8 (13.8% share) to 11 (20% share), and possible injury crashes (severity C) increased from 1 (1.7% share) to 7 (12.7% share).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Most severe injury per crash record
Top Contributing Factors
The top contributing factor, 'Failed to yield right of way,' decreased by 61.5% in count, from 13 crashes in October 2021 to 5 crashes in October 2022, falling from first to third rank. 'Inattention' decreased by 11.1% in count, from 9 to 8 crashes, becoming the top factor in October 2022. 'Failure to keep in proper lane or running off road' increased by 50% in count, from 4 to 6 crashes, rising from fifth to second rank.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The proportion of crashes occurring in 'Clear' weather conditions decreased from 79.3% (46 of 58 crashes) in October 2021 to 60% (33 of 55 crashes) in October 2022. Concurrently, crashes on 'Wet' road surfaces increased from 9 to 15, raising their proportion from 15.5% to 27.3%. Crashes during 'Daylight' conditions decreased from 41 to 31, while crashes in 'Dark - lighted roadway' conditions increased from 6 to 16.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Road surface condition field
Vehicles & Demographics
The ranking of top vehicle makes involved in crashes shifted, with Ford rising from 9 vehicles in October 2021 to 17 vehicles in October 2022, becoming the most frequent. Toyota dropped from 17 to 11 vehicles, while Honda remained a prominent make, decreasing slightly from 14 to 13 vehicles. The 16-20 age group saw an increase in persons involved, from 21 to 26, while the 26-34 age group decreased from 23 to 7 persons.
Top Vehicle Makes (92 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Vehicle unit records
8 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (101 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in the 30 mph speed zone decreased from 23 to 13, and this zone no longer reported any fatalities, down from 1 in the prior period. Crashes in the 35 mph zone increased from 14 to 17, and in the 40 mph zone, they doubled from 6 to 12. The 65 mph zone saw a decrease in crashes from 10 to 5, and new crashes were reported in 20 mph (1), 45 mph (3), and 50 mph (1) zones, which had no reported crashes in the prior period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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: 2022-10-01 through 2022-10-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-10-01 through 2022-10-31 (31 days)
- Geographic scope: DARTMOUTH, MA
- Total crash records analyzed: 55
- Total persons involved: 111
- Total vehicles involved: 92
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: October 2022." Published June 21, 2026. Reporting period: 2022-10-01 to 2022-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/dartmouth/october-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2022-10-01 – 2022-10-31
Generated: June 21, 2026 · All rights reserved