ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · NEW BEDFORD, MA · APRIL 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/april-2025-report
Monthly Traffic Safety Analysis
299 CRASHES IN
NEW BEDFORD, MA
APRIL 2025
In April 2025, NEW BEDFORD experienced 299 crashes, an increase from 280 crashes in April 2024, marking a 6.79% rise year-over-year. The most significant shift was in total injuries, which surged by 124% from 50 in the prior period to 112 in the current period.
299
▲ 6.8%was 280
Total Crash Events
0
Persons Killed
112
▲ 124.0%was 50
Persons Injured
46
▼ -4.2%was 48
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. 17 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash incidents in NEW BEDFORD increased year-over-year, with total crashes rising by 6.79% from 280 in April 2024 to 299 in April 2025. This increase was accompanied by a substantial 124% rise in total injuries, from 50 to 112, indicating a worsening trend in crash outcomes.
46
Hit-and-Run Crashes — April 2025
▼ -4.2% vs prior (48)
The number of hit-and-run crashes decreased by 4.17%, from 48 incidents in April 2024 to 46 in April 2025. Consequently, the hit-and-run rate also saw a decline, dropping by 1.7 percentage points from 17.1% to 15.4% of total crashes. This indicates a downward trend in hit-and-run incidents year-over-year.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
3
Pedestrians Injured
6
Cyclists Injured
103
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · 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 Monday in April 2024 (51 crashes) to Wednesday in April 2025 (53 crashes). While the peak hour remained at 24 crashes, it shifted from 8 AM in the prior period to 7 AM in the current period. Notably, crashes on Wednesday saw a significant increase from 27 to 53 year-over-year, while Monday crashes decreased from 51 to 42.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes remained at zero in both April 2024 and April 2025. However, serious injury crashes (severity A) increased from 1 to 5, representing a 400% rise. Minor injury crashes (severity B) also saw a substantial increase, growing by 92% from 25 to 48, while possible injury crashes (severity C) decreased by 10.5% from 19 to 17.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor, 'No improper driving,' increased by 15 crashes, from 120 in April 2024 to 135 in April 2025, while its share of total crashes rose from 42.9% to 45.2%. Crashes attributed to 'Distracted' driving saw a substantial 133.3% increase, rising from 3 to 7 incidents. Conversely, 'Failed to yield right of way' crashes decreased by 17.9%, from 28 to 23, and 'Disregarded traffic signs, signals, road markings' decreased by 54.5%, from 11 to 5 incidents.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in 'Clear' weather remained constant at 194 incidents in both periods, but crashes during 'Rain' increased by 72.7%, from 22 to 38. There was a notable shift in lighting conditions, with 'Daylight' crashes increasing by 20.1% (from 184 to 221) and crashes in 'Dark - lighted roadway' decreasing by 19.4% (from 67 to 54). Additionally, crashes on 'Wet' road surfaces rose by 52.2%, from 46 to 70 incidents.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased by 7.86%, from 560 to 604 year-over-year. All reported age groups saw an increase in persons involved, with the 0-15 age group showing a 104.5% increase from 22 to 45, and the 55-64 age group increasing by 52.2% from 46 to 70. Toyota remained the top vehicle make involved, though its count slightly decreased from 98 to 96, while Honda and Ford saw increases and rose in ranking.
Top Vehicle Makes (604 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Vehicle unit records
155 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (576 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes in the 30 mph speed zone remained the most frequent, increasing slightly from 173 to 176 incidents year-over-year. Conversely, crashes in the 25 mph zone decreased by 13.7%, from 51 to 44. Notably, crashes in the 65 mph speed zone saw a significant increase of 125%, rising from 4 to 9 incidents. No fatalities were recorded in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · 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: 2025-04-01 through 2025-04-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-04-01 through 2025-04-30 (30 days)
- Geographic scope: NEW BEDFORD, MA
- Total crash records analyzed: 299
- Total persons involved: 748
- Total vehicles involved: 604
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: April 2025." Published June 21, 2026. Reporting period: 2025-04-01 to 2025-04-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/new-bedford/april-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: 2025-04-01 – 2025-04-30
Generated: June 21, 2026 · All rights reserved