ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · NEW BEDFORD, MA · MARCH 2025
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/march-2025-report
Monthly Traffic Safety Analysis
266 CRASHES IN
NEW BEDFORD, MA
MARCH 2025
In March 2025, New Bedford experienced 266 crashes, a 6.7% decrease from the 285 crashes reported in March 2024. Total injuries also saw a notable decline, dropping from 74 to 63. The most significant year-over-year shift was an 80% increase in DUI crashes, rising from 5 to 9 incidents.
266
▼ -6.7%was 285
Total Crash Events
0
Persons Killed
63
▼ -14.9%was 74
Persons Injured
53
▲ 26.2%was 42
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. 19 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash data for March indicates a downward trend in New Bedford, with total crashes decreasing by 6.7% from 285 in the prior year to 266 in the current year. Similarly, total injuries declined by 14.9%, from 74 to 63, suggesting a general improvement in safety outcomes.
53
Hit-and-Run Crashes — March 2025
▲ 26.2% vs prior (42)
Hit-and-run crashes increased from 42 incidents in March 2024 to 53 incidents in March 2025, marking a 26.2% rise. This led to an increase in the hit-and-run rate, which grew from 14.7% to 19.9% of all crashes year-over-year.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
0
Other Killed
5
Pedestrians Injured
1
Cyclists Injured
56
Motorists Injured
1
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal distribution of crashes shifted year-over-year, with the peak day moving from Friday with 53 crashes in March 2024 to Monday with 46 crashes in March 2025. The peak hour for crashes also changed from 2 p.m. to 3 p.m., although both hours recorded 27 crashes.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatal crashes in either period, maintaining a fatal crash rate of 0%. Crashes resulting in serious injuries decreased from 6 (2.1% of crashes) in March 2024 to 4 (1.5% of crashes) in March 2025. Overall, the proportion of crashes resulting in any injury (serious, minor, or possible) decreased from 21.4% to 16.9%, while 'No Injury' crashes increased from 69.5% to 75.9%.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Most severe injury per crash record
Top Contributing Factors
The count of crashes attributed to 'No improper driving' increased by 25.9%, from 85 in the prior period to 107 in the current period. Conversely, 'Inattention' as a contributing factor saw a 60.5% decrease in count, falling from 38 to 15 crashes. 'Failed to yield right of way' increased by 42.9% in count, rising from 14 to 20 crashes.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring during rainy weather conditions decreased significantly from 36 in March 2024 to 14 in March 2025, representing a 61.1% reduction. Correspondingly, crashes on wet road surfaces declined by 43.1%, from 58 to 33. Crashes in clear weather increased slightly by 2.9% from 175 to 180, and on dry surfaces by 4.1% from 221 to 230.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased by 6.0%, from 566 in March 2024 to 532 in March 2025. While the top five vehicle makes (Toyota, Honda, Ford, Chevrolet, Nissan) remained consistent in ranking, each saw a decrease in their crash involvement counts. Notably, crashes involving persons aged 16-20 decreased by 42.6% (from 68 to 39), while those involving persons aged 65+ increased by 48.9% (from 45 to 67).
Top Vehicle Makes (532 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Vehicle unit records
137 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (481 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes occurring in 30 mph speed zones saw a slight increase of 2.6%, rising from 156 to 160. A significant decrease of 83.3% was observed in 65 mph zones, with crashes dropping from 12 to 2. Crashes in 25 mph zones decreased by 8.2%, from 49 to 45 incidents.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · 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: 2025-03-01 through 2025-03-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-03-01 through 2025-03-31 (31 days)
- Geographic scope: NEW BEDFORD, MA
- Total crash records analyzed: 266
- Total persons involved: 632
- Total vehicles involved: 532
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: March 2025." Published June 21, 2026. Reporting period: 2025-03-01 to 2025-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/new-bedford/march-2025-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: 2025-03-01 – 2025-03-31
Generated: June 21, 2026 · All rights reserved