ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · NEW BEDFORD, MA · JUNE 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/new-bedford/june-2022-report
Monthly Traffic Safety Analysis
333 CRASHES IN
NEW BEDFORD, MA
JUNE 2022
In June 2022, NEW BEDFORD, MA experienced 333 total crashes, a decrease of 2.9% compared to the 343 crashes in June 2021. A significant year-over-year shift is the absence of fatalities in June 2022, down from 2 fatalities in June 2021. However, hit-and-run crashes saw a substantial increase from 9 to 25.
333
▼ -2.9%was 343
Total Crash Events
0
▼ -100.0%was 2
Persons Killed
89
▼ -13.6%was 103
Persons Injured
25
▲ 177.8%was 9
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. 29 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, total crashes in NEW BEDFORD decreased slightly by 2.9%, from 343 crashes in June 2021 to 333 crashes in June 2022. This indicates a relatively stable trend in crash occurrences year-over-year for this period.
25
Hit-and-Run Crashes — June 2022
▲ 177.8% vs prior (9)
Hit-and-run crashes increased substantially from 9 in June 2021 to 25 in June 2022, representing a rise of 16 crashes. Consequently, the hit-and-run rate more than doubled, increasing from 2.6% to 7.5% of all crashes, indicating an upward trend.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
7
Pedestrians Injured
4
Cyclists Injured
78
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak crash day shifted from Monday (58 crashes) in June 2021 to Thursday and Monday (both 57 crashes) in June 2022, with Wednesday crashes notably decreasing from 57 to 40. The peak crash hour remained 3 PM, though the count decreased from 36 crashes in June 2021 to 31 crashes in June 2022. There was a notable increase in crashes between 9 PM and 11 PM, rising from a combined 17 crashes in June 2021 to 33 crashes in June 2022.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes decreased significantly from 2 in June 2021 to 0 in June 2022. Total injuries also saw a reduction, dropping from 103 to 89. Crashes resulting in minor injuries (B) increased from 37 (10.8% share) to 47 (14.1% share), while possible injury crashes (C) decreased from 34 (9.9% share) to 23 (6.9% share).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Most severe injury per crash record
Top Contributing Factors
Crashes attributed to 'No improper driving' decreased by 7, from 107 to 100. 'Inattention' increased by 5 crashes, from 29 to 34, while 'Followed too closely' more than doubled, rising from 6 to 15 crashes. Conversely, 'Other improper action' decreased by 11 crashes, from 21 to 10, and 'Failure to keep in proper lane or running off road' decreased by 6 crashes, from 10 to 4.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The number of crashes occurring in 'Clear' weather conditions increased from 258 to 268, while crashes in 'Rain' decreased from 16 to 7. Crashes during 'Daylight' decreased from 280 to 261, but crashes in 'Dark - lighted roadway' increased from 37 to 47. The number of crashes on 'Wet' road surfaces decreased from 25 to 14, while 'Dry' road surface crashes remained constant at 315.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 633 to 655. Toyota remained the top make involved, increasing from 98 to 103, while Honda decreased from 87 to 80. Kia saw a notable decrease in involvement, dropping from 37 to 21 vehicles. In terms of persons involved, the 16-20 age group saw a decrease from 76 to 58, while the 26-34 age group increased from 139 to 148.
Top Vehicle Makes (655 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Vehicle unit records
173 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (611 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 30 mph speed zones saw a slight decrease from 223 to 218, remaining the most common speed zone for crashes. Crashes in 25 mph zones increased from 41 to 47, and those in 65 mph zones increased from 11 to 15. Notably, there were 0 fatal crashes in June 2022, compared to 2 fatal crashes in 30 mph zones in June 2021.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · 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: 2022-06-01 through 2022-06-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-06-01 through 2022-06-30 (30 days)
- Geographic scope: NEW BEDFORD, MA
- Total crash records analyzed: 333
- Total persons involved: 790
- Total vehicles involved: 655
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). "NEW BEDFORD, MA Crash Intelligence Report: June 2022." Published June 21, 2026. Reporting period: 2022-06-01 to 2022-06-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/new-bedford/june-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: 2022-06-01 – 2022-06-30
Generated: June 21, 2026 · All rights reserved