ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · NEW BEDFORD, MA · 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/2022-annual-report
Yearly Traffic Safety Analysis
3,836 CRASHES IN
NEW BEDFORD, MA
2022
In 2022, New Bedford recorded 3,836 traffic crashes, a 1.8% decrease from the 3,907 crashes reported in 2021. While overall crashes and fatalities declined, the most notable year-over-year shift was a significant increase in hit-and-run incidents, which more than doubled from 167 in 2021 to 354 in 2022.
3,836
▼ -1.8%was 3,907
Total Crash Events
4
▼ -55.6%was 9
Persons Killed
1,126
▼ -4.0%was 1,173
Persons Injured
354
▲ 112.0%was 167
Hit-and-Run Crashes
Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 473 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in traffic incidents shows a slight decrease. Total crashes fell by 1.8% from 3,907 in 2021 to 3,836 in 2022. Similarly, the number of people injured decreased by 4.0% from 1,173 to 1,126, and total fatalities saw a substantial drop from 9 to 4 year-over-year.
354
Hit-and-Run Crashes — 2022
▲ 112.0% vs prior (167)
Hit-and-run crashes increased substantially in 2022 compared to the prior year. The number of hit-and-run incidents more than doubled, rising from 167 in 2021 to 354 in 2022. Consequently, the hit-and-run rate, as a percentage of all crashes, also more than doubled from 4.3% to 9.2%, indicating a strong upward trend.
Vulnerable Road User Casualties
2
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
0
Other Killed
62
Pedestrians Injured
24
Cyclists Injured
1,034
Motorists Injured
6
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes remained largely consistent between the two periods. Friday was the peak day for crashes in both 2022 (621 crashes) and 2021 (627 crashes). The peak hour for incidents saw a minor shift, moving from 4 PM in 2021 (346 crashes) to 3 PM in 2022 (332 crashes), indicating the afternoon commute remains the most frequent time for collisions.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity decreased notably year-over-year, driven by a reduction in fatal incidents. The number of fatal crashes was halved from 8 in 2021 to 4 in 2022, with the fatal crash rate dropping from 0.2% to 0.1%. The proportion of crashes resulting in any injury (Serious, Minor, or Possible) remained stable, accounting for 21.2% of crashes in 2022 compared to 21.3% in 2021.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors to crashes were unchanged between periods, with 'No improper driving', 'Inattention', and 'Failed to yield right of way' ranking as the top three in both years. However, the count for some factors shifted; crashes attributed to 'Failed to yield right of way' increased by 14.1% from 213 to 243 incidents. The count for 'Distracted' driving also grew by 25.0%, from 64 to 80 crashes.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The conditions under which crashes occurred saw minimal change between 2021 and 2022. In both years, the majority of incidents happened in 'Daylight' (66.0% in 2021, 67.5% in 2022) and on 'Dry' road surfaces (82.7% in 2021, 81.6% in 2022). There was no significant shift in the proportion of crashes occurring during adverse weather or lighting conditions.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes remained consistent, with Toyota, Honda, and Ford being the top three in both 2021 and 2022. Regarding the age of persons involved, there was a slight demographic shift; the share of persons aged 16-25 decreased from a combined 20.1% in 2021 to 17.0% in 2022. Conversely, the proportion of persons aged 65 and older increased slightly from 7.3% to 7.6%.
Top Vehicle Makes (7,577 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
2,093 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (7,063 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones was similar year-over-year, with the 30 mph zone accounting for the largest number of incidents in both 2022 (2,400 crashes) and 2021 (2,465 crashes). Fatal crashes were distributed across various speed limits in both periods. Notably, fatalities in 30 mph zones decreased from 3 to 1, while two fatal crashes occurred in the 50 mph zone in 2022, where none were recorded in the prior year.
Fatal crashes by zone: 30 mph: 1 of 2,400 (0.042%) · 50 mph: 2 of 33 (6.061%) · 65 mph: 1 of 142 (0.704%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: NEW BEDFORD, MA
- Total crash records analyzed: 3,836
- Total persons involved: 9,294
- Total vehicles involved: 7,577
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: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/new-bedford/2022-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved